Conversation
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| # Ensure act import if state updates are detected | ||
| if "act(" in result and "import" in result and "act" not in result.split("from '@testing-library/react'")[0]: | ||
| result = result.replace("from '@testing-library/react'", "act, " + "from '@testing-library/react'", 1) |
There was a problem hiding this comment.
Bug: This act injection produces invalid JavaScript syntax.
Given import { render, screen } from '@testing-library/react', this replacement produces:
import { render, screen } act, from '@testing-library/react'
The fix should insert act inside the braces:
| result = result.replace("from '@testing-library/react'", "act, " + "from '@testing-library/react'", 1) | |
| result = result.replace("} from '@testing-library/react'", ", act } from '@testing-library/react'", 1) |
| return source | ||
|
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| jsx_content = source_bytes[jsx_start:jsx_end].decode("utf-8").strip() | ||
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| # Check if the return uses parentheses: return (...) | ||
| # If so, we need to wrap inside the parens | ||
| has_parens = False | ||
| for child in return_node.children: | ||
| if child.type == "parenthesized_expression": | ||
| has_parens = True | ||
| jsx_start = child.start_byte + 1 # skip ( | ||
| jsx_end = child.end_byte - 1 # skip ) | ||
| jsx_content = source_bytes[jsx_start:jsx_end].decode("utf-8").strip() | ||
| break | ||
|
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| wrapped = ( | ||
| f'<React.Profiler id="{profiler_id}" onRender={{_codeflashOnRender_{safe_name}}}>' | ||
| f"\n{jsx_content}\n" | ||
| f"</React.Profiler>" | ||
| ) |
There was a problem hiding this comment.
Bug: Byte offsets from tree-sitter (start_byte/end_byte) are used as string indices on source (Python str). This works for ASCII-only source but will produce corrupted output if the source contains multi-byte UTF-8 characters (e.g., Unicode in comments or string literals).
Line 192 correctly uses source_bytes[jsx_start:jsx_end], but line 211 incorrectly uses source[:jsx_start] and source[jsx_end:] with byte offsets on a string.
Fix: Use source_bytes for slicing and decode back:
return source_bytes[:jsx_start].decode("utf-8") + wrapped + source_bytes[jsx_end:].decode("utf-8")The same byte-offset vs string-index mismatch exists in _insert_after_imports (line 225-229).
| def _node_has_return(self, node: Node) -> bool: | ||
| """Recursively check if a node contains a return statement.""" | ||
| if node.type == "return_statement": | ||
| return True | ||
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| # Don't recurse into nested function definitions | ||
| if node.type in ("function_declaration", "function_expression", "arrow_function", "method_definition"): | ||
| # Only check the current function, not nested ones | ||
| body_node = node.child_by_field_name("body") | ||
| if body_node: | ||
| for child in body_node.children: | ||
| if self._node_has_return(child): | ||
| return True | ||
| return False | ||
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| return any(self._node_has_return(child) for child in node.children) |
There was a problem hiding this comment.
Bug: _node_has_return incorrectly reports returns from nested functions as belonging to the outer function.
When called on a top-level function, it traverses body children via _node_has_return(child). If child is a nested function, it enters the same function-type branch at line 1236 and searches that nested function's body for returns. A return inside a nested function would propagate True back to the caller, making it appear the outer function has a return.
Example:
function outer() { // No return here
function inner() {
return 42; // This return belongs to inner, not outer
}
}has_return_statement(outer) would incorrectly return True.
Fix: when encountering a nested function node (not the initial call), skip it:
def has_return_statement(self, function_node, source):
body = function_node.node.child_by_field_name("body")
if body:
return self._node_has_return(body)
return False
def _node_has_return(self, node):
if node.type == "return_statement":
return True
if node.type in ("function_declaration", "function_expression", "arrow_function", "method_definition"):
return False # Skip nested functions
return any(self._node_has_return(child) for child in node.children)| def _ensure_react_import(source: str) -> str: | ||
| """Ensure React is imported (needed for React.Profiler).""" | ||
| if "import React" in source or "import * as React" in source: | ||
| return source | ||
| # Add React import at the top | ||
| if "from 'react'" in source or 'from "react"' in source: | ||
| # React is imported but maybe not as the default. That's fine for JSX. | ||
| # We need React.Profiler so add it | ||
| if "React" not in source.split("from", maxsplit=1)[0] if "from" in source else "": | ||
| return 'import React from "react";\n' + source | ||
| return source | ||
| return 'import React from "react";\n' + source |
There was a problem hiding this comment.
Issue: _ensure_react_import has fragile string matching logic.
Line 244's ternary is hard to read and has an edge case: source.split("from", maxsplit=1)[0] splits the entire file on the first occurrence of "from" which could be from a different import, a variable name, or a comment — not necessarily the React import.
For example, if the file starts with:
import { transform } from 'babel';
import { useState } from 'react';The split would be on 'babel''s from, and the check would look for "React" before import { transform }, which wouldn't find it, causing a duplicate import.
Consider matching the specific React import line with a regex instead:
react_import_re = re.compile(r"import\s+.*\bfrom\s+['\"]react['\"]")| start_line=node.start_point[0] + 1, | ||
| end_line=node.end_point[0] + 1, | ||
| ) | ||
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| def _process_import_clause( | ||
| self, | ||
| node: Node, | ||
| source_bytes: bytes, | ||
| default_import: str | None, | ||
| named_imports: list[tuple[str, str | None]], | ||
| namespace_import: str | None, | ||
| ) -> None: | ||
| """Process an import clause to extract imports.""" | ||
| # This is a helper that modifies the lists in place | ||
| # Processing is done inline in _extract_import_info |
There was a problem hiding this comment.
Dead code: _process_import_clause is called on line 527 but the method body is empty (only has a docstring comment). All actual processing happens in the for clause_child in child.children loop on lines 529-545 that follows the call.
This dead method should be removed to avoid confusion. If it's intended as a future extension point, add a pass statement and a clear # TODO explaining the intent.
PR Review SummaryPrek ChecksAll checks pass. Ruff check and ruff format both passed after fixes. Mypy Type ErrorsFixed 17 of 26 errors. Committed and pushed fixes in
Remaining unfixable errors (9):
Code ReviewAll 8 previously reported issues are still present in the latest code:
Additional finding: Test Coverage
Overall coverage: 79% Coverage flags:
Optimization PRs
Last updated: 2026-02-20T12:15:00Z |
⚡️ Codeflash found optimizations for this PR📄 36% (0.36x) speedup for
|
| for match in hook_pattern.finditer(component_source): | ||
| hook_name = match.group(1) | ||
| # Try to determine if there's a dependency array | ||
| # Look for ], [ pattern after the hook call (simplified heuristic) | ||
| rest_of_line = component_source[match.end() :] | ||
| has_deps = False | ||
| dep_count = 0 | ||
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| # Simple heuristic: count brackets to find dependency array | ||
| bracket_depth = 1 | ||
| for i, char in enumerate(rest_of_line): | ||
| if char == "(": | ||
| bracket_depth += 1 | ||
| elif char == ")": | ||
| bracket_depth -= 1 | ||
| if bracket_depth == 0: | ||
| # Check if the last argument before closing paren is an array | ||
| preceding = rest_of_line[:i].rstrip() | ||
| if preceding.endswith("]"): | ||
| has_deps = True | ||
| # Count items in the array (rough: count commas + 1 for non-empty) | ||
| array_start = preceding.rfind("[") | ||
| if array_start >= 0: | ||
| array_content = preceding[array_start + 1 : -1].strip() | ||
| if array_content: | ||
| dep_count = array_content.count(",") + 1 | ||
| else: | ||
| dep_count = 0 # empty deps [] | ||
| has_deps = True | ||
| break | ||
|
|
There was a problem hiding this comment.
⚡️Codeflash found 17% (0.17x) speedup for _extract_hook_usages in codeflash/languages/javascript/frameworks/react/context.py
⏱️ Runtime : 4.10 milliseconds → 3.52 milliseconds (best of 121 runs)
📝 Explanation and details
This optimization achieves a 16% runtime improvement by eliminating repeated string slicing operations that create temporary substring objects on every hook match.
Key changes:
-
Avoided expensive slicing: The original code created
rest_of_line = component_source[match.end():]for every hook found, copying the remainder of the source string. The optimized version uses absolute indices (start_indexandj) to scan the original string directly without creating temporary substrings. -
Replaced
enumerate()with manual indexing: Changed fromfor i, char in enumerate(rest_of_line)towhile j < s_lenwith manual character access vias[j]. This reduces Python-level overhead from the enumerate iterator and avoids the cost of iterating over a freshly sliced string. -
Backward scanning instead of full slice: Instead of creating
preceding = rest_of_line[:i].rstrip()to check for trailing], the code now scans backward from positionj-1to skip whitespace, checking only the minimal necessary characters. This avoids creating and processing another temporary substring. -
Bounded
rfind()call: The optimized version usess.rfind("[", start_index, k + 1)with explicit bounds rather than searching the entireprecedingslice, making the search more efficient and avoiding the slice allocation.
Performance characteristics:
-
Large inputs see the biggest gains: The test
test_large_number_of_hooks_performance_and_correctness(1000 hooks) shows 42.9% speedup (1.42ms → 994μs), andtest_performance_with_many_non_matching_patterns(500 hooks) shows 56.3% speedup (806μs → 515μs). This is because the per-match slicing overhead compounds dramatically with more hooks. -
Small inputs show modest variation: Tests with 1-3 hooks show mixed results (±2-11%), as the fixed overhead of compilation and setup dominates when there are few matches. The optimization's benefit is most visible when the parsing loop executes many times.
-
Trade-off for dependency parsing: Some tests with complex dependency arrays show slight slowdowns (e.g.,
test_hooks_with_many_dependenciesis 23.5% slower), likely because the boundedrfind()or backward whitespace scanning adds small overhead in edge cases. However, the overall 16% improvement across the full benchmark suite indicates these are outweighed by the gains in the common case.
Why this matters: In React codebases with many hook calls per component, this optimization significantly reduces the parser's memory footprint and improves throughput, making hook analysis faster at scale.
✅ Correctness verification report:
| Test | Status |
|---|---|
| ⚙️ Existing Unit Tests | 🔘 None Found |
| 🌀 Generated Regression Tests | ✅ 58 Passed |
| ⏪ Replay Tests | 🔘 None Found |
| 🔎 Concolic Coverage Tests | 🔘 None Found |
| 📊 Tests Coverage | 100.0% |
🌀 Click to see Generated Regression Tests
import pytest # used for our unit tests
# import the function and the HookUsage type from the real module under test
from codeflash.languages.javascript.frameworks.react.context import (
HookUsage, _extract_hook_usages)
def test_no_hooks_returns_empty_list():
# Very simple source with no hook-like identifiers -> expect empty list
src = "function Component() { const x = 1; return x; }"
codeflash_output = _extract_hook_usages(src); result = codeflash_output # 4.52μs -> 4.80μs (5.86% slower)
def test_single_hook_no_deps():
# Single hook call with simple argument, no dependency array
src = "const [s, setS] = useState(0);"
codeflash_output = _extract_hook_usages(src); result = codeflash_output # 8.19μs -> 7.36μs (11.1% faster)
hu = result[0]
def test_single_hook_with_empty_deps():
# Hook with an explicit empty dependency array
src = "useEffect(() => { doSomething(); }, [])"
codeflash_output = _extract_hook_usages(src); result = codeflash_output # 10.3μs -> 10.6μs (2.65% slower)
hu = result[0]
def test_single_hook_with_multiple_deps():
# Hook with several comma-separated dependencies
src = "useEffect(() => { a(); }, [a, b, c])"
codeflash_output = _extract_hook_usages(src); result = codeflash_output # 10.3μs -> 10.5μs (2.67% slower)
hu = result[0]
def test_hook_with_nested_parentheses_and_deps():
# Ensure nested parentheses in the first argument do not break detection
src = "useMemo(() => (x) => (y) => y + x, [dep])"
codeflash_output = _extract_hook_usages(src); result = codeflash_output # 10.9μs -> 10.9μs (0.558% slower)
hu = result[0]
def test_multiple_hooks_mixed_in_one_source():
# Multiple hooks of different kinds in a single source text,
# including one with deps and one without
src = """
function C() {
const [s] = useState(0);
useEffect(() => { console.log(s); }, [s]);
useRef(null);
}
"""
codeflash_output = _extract_hook_usages(src); result = codeflash_output # 15.5μs -> 15.1μs (2.39% faster)
def test_hook_name_case_sensitivity_and_digits():
# The regex requires an uppercase letter after 'use'. Lowercase should not match.
src = "useeffect(); useA1('x');"
codeflash_output = _extract_hook_usages(src); result = codeflash_output # 8.22μs -> 7.36μs (11.7% faster)
def test_dependency_array_on_next_line_and_spaces():
# Dependency array may be on the next line or with extra spaces
src = "useEffect(fn, \n [dep] )"
codeflash_output = _extract_hook_usages(src); result = codeflash_output # 9.60μs -> 9.75μs (1.54% slower)
hu = result[0]
def test_false_positive_array_in_inner_argument_counts_as_deps():
# The heuristic looks for a trailing ']' before the closing paren.
# If an inner argument contains an array (but it's not the last arg),
# the heuristic may still treat it as the dependency array.
# This test documents that behavior: the function will mark has_dependency_array True.
src = "useCustom(doSomething([a,b]))"
codeflash_output = _extract_hook_usages(src); result = codeflash_output # 8.87μs -> 8.57μs (3.50% faster)
hu = result[0]
def test_no_match_for_lowercase_after_use():
# Confirm that 'use' followed by a lowercase character is not matched
src = "const x = usevalue(1);"
codeflash_output = _extract_hook_usages(src); result = codeflash_output # 4.24μs -> 4.37μs (2.98% slower)
def test_empty_input_returns_empty_list():
# Edge case: empty source string should return no hooks
codeflash_output = _extract_hook_usages(""); result = codeflash_output # 3.06μs -> 3.15μs (2.86% slower)
def test_large_number_of_hooks_performance_and_correctness():
# Construct a large source string with 1000 simple hook calls (no deps)
count = 1000
# Use the correct capitalization 'useState' which the regex will match
src = ";".join("useState({})".format(i) for i in range(count))
codeflash_output = _extract_hook_usages(src); result = codeflash_output # 1.42ms -> 994μs (42.9% faster)
def test_large_mixed_hooks_with_deps_and_nested_parens():
# Build a large source alternating hooks with and without deps,
# and some with nested parentheses to ensure the parser remains correct.
items = []
n = 300 # smaller than 1000 but still large and exercises loops
for i in range(n):
if i % 3 == 0:
# hook with deps
items.append(f"useEffect(() => {{ fn({i}); }}, [d{i}, d{i+1}])")
elif i % 3 == 1:
# hook with nested parentheses but no deps
items.append(f"useMemo(() => ((x) => x + {i})())")
else:
# simple hook
items.append(f"useRef(null)")
src = ";\n".join(items)
codeflash_output = _extract_hook_usages(src); result = codeflash_output # 706μs -> 749μs (5.70% slower)
# Validate a few sampled positions for expected properties
for i in range(0, n, 50):
hu = result[i]
if i % 3 == 0:
pass
elif i % 3 == 1:
pass
else:
pass
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.from dataclasses import dataclass
# imports
import pytest
from codeflash.languages.javascript.frameworks.react.context import \
_extract_hook_usages
def test_single_hook_with_dependency_array():
"""Test extraction of a single hook with a dependency array."""
source = "useState(initialValue, [value])"
codeflash_output = _extract_hook_usages(source); result = codeflash_output # 10.8μs -> 10.8μs (0.566% faster)
def test_single_hook_without_dependency_array():
"""Test extraction of a hook without a dependency array."""
source = "useEffect(() => { console.log('mounted'); })"
codeflash_output = _extract_hook_usages(source); result = codeflash_output # 10.2μs -> 10.3μs (0.594% slower)
def test_multiple_hooks_mixed():
"""Test extraction of multiple hooks with mixed dependency array presence."""
source = "useState(0, []) useEffect(() => {}) useContext(MyContext)"
codeflash_output = _extract_hook_usages(source); result = codeflash_output # 13.6μs -> 13.0μs (4.23% faster)
def test_hook_with_multiple_dependencies():
"""Test extraction of a hook with multiple dependencies."""
source = "useState(initialValue, [dep1, dep2, dep3])"
codeflash_output = _extract_hook_usages(source); result = codeflash_output # 10.5μs -> 11.0μs (4.01% slower)
def test_hook_with_complex_dependency_expressions():
"""Test extraction of a hook with complex dependency expressions."""
source = "useCallback(() => {}, [state.prop, obj.method(), getValue()])"
codeflash_output = _extract_hook_usages(source); result = codeflash_output # 11.7μs -> 12.5μs (6.57% slower)
def test_hook_in_assignment():
"""Test extraction of a hook used in an assignment."""
source = "const state = useState(0, [counter])"
codeflash_output = _extract_hook_usages(source); result = codeflash_output # 9.55μs -> 9.66μs (1.14% slower)
def test_hook_with_whitespace_variations():
"""Test extraction of hooks with varying whitespace."""
source = "useEffect ( callback , [ dep ] )"
codeflash_output = _extract_hook_usages(source); result = codeflash_output # 9.94μs -> 10.4μs (4.71% slower)
def test_builtin_function_not_matched():
"""Test that built-in functions like useState are not confused with user functions."""
source = "useState(0) versus myFunction()"
codeflash_output = _extract_hook_usages(source); result = codeflash_output # 8.14μs -> 7.49μs (8.57% faster)
def test_hook_with_string_literals_containing_parens():
"""Test extraction when hook dependencies contain string literals with parentheses."""
source = "useMemo(() => compute(), ['key(value)'])"
codeflash_output = _extract_hook_usages(source); result = codeflash_output # 10.6μs -> 10.8μs (2.50% slower)
def test_custom_hook_extraction():
"""Test extraction of custom hooks following the useXxx naming convention."""
source = "useCustomHook(param1, [dep1, dep2])"
codeflash_output = _extract_hook_usages(source); result = codeflash_output # 9.62μs -> 9.84μs (2.24% slower)
def test_empty_source_string():
"""Test extraction from an empty source string."""
source = ""
codeflash_output = _extract_hook_usages(source); result = codeflash_output # 3.01μs -> 3.30μs (8.80% slower)
def test_source_with_no_hooks():
"""Test extraction from source containing no hooks."""
source = "const x = 42; function regularFunction() { return x; }"
codeflash_output = _extract_hook_usages(source); result = codeflash_output # 4.40μs -> 4.78μs (7.97% slower)
def test_hook_at_end_of_string_without_closing_paren():
"""Test extraction when hook call is incomplete at end of string."""
source = "useState("
codeflash_output = _extract_hook_usages(source); result = codeflash_output # 6.66μs -> 6.36μs (4.73% faster)
def test_hook_with_empty_dependency_array():
"""Test extraction of a hook with explicitly empty dependency array."""
source = "useEffect(callback, [])"
codeflash_output = _extract_hook_usages(source); result = codeflash_output # 8.93μs -> 9.06μs (1.44% slower)
def test_hook_with_only_whitespace_in_dependency_array():
"""Test extraction of a hook with only whitespace inside dependency array."""
source = "useEffect(callback, [ ])"
codeflash_output = _extract_hook_usages(source); result = codeflash_output # 9.37μs -> 9.27μs (1.09% faster)
def test_nested_function_calls_in_hook():
"""Test extraction when hook arguments contain nested function calls."""
source = "useState(getValue(getInitialValue()))"
codeflash_output = _extract_hook_usages(source); result = codeflash_output # 9.34μs -> 9.29μs (0.538% faster)
def test_hook_with_nested_arrays_in_dependencies():
"""Test extraction when dependency array contains nested arrays."""
source = "useCallback(fn, [[nested, array], item])"
codeflash_output = _extract_hook_usages(source); result = codeflash_output # 10.3μs -> 10.5μs (2.37% slower)
def test_hook_name_case_sensitivity():
"""Test that hook matching respects case sensitivity."""
source = "usestate(0) useState(0)"
codeflash_output = _extract_hook_usages(source); result = codeflash_output # 7.82μs -> 7.34μs (6.54% faster)
def test_hook_not_matched_without_capital_letter_after_use():
"""Test that use1stHook pattern is correctly identified."""
source = "use1stHook(value, [dep])"
codeflash_output = _extract_hook_usages(source); result = codeflash_output # 4.02μs -> 4.33μs (7.16% slower)
def test_hook_with_no_arguments():
"""Test extraction of a hook called with no arguments."""
source = "useContext()"
codeflash_output = _extract_hook_usages(source); result = codeflash_output # 7.35μs -> 6.63μs (10.9% faster)
def test_multiple_hooks_on_same_line():
"""Test extraction of multiple hooks on the same line with various patterns."""
source = "const a = useState(0, [x]); const b = useEffect(() => {}, [y, z])"
codeflash_output = _extract_hook_usages(source); result = codeflash_output # 13.6μs -> 13.4μs (1.73% faster)
def test_hook_immediately_followed_by_another():
"""Test extraction when hooks are written consecutively."""
source = "useState(0, [])useEffect(() => {}, [x])"
codeflash_output = _extract_hook_usages(source); result = codeflash_output # 12.3μs -> 12.1μs (1.99% faster)
def test_hook_with_arrow_function_in_dependencies():
"""Test extraction when dependency array contains arrow functions (unusual but possible)."""
source = "useCallback(() => {}, [() => null, other])"
codeflash_output = _extract_hook_usages(source); result = codeflash_output # 10.2μs -> 11.0μs (6.93% slower)
def test_hook_with_object_literal_in_dependencies():
"""Test extraction when dependency array contains object literals."""
source = "useMemo(() => {}, [{key: value}, other])"
codeflash_output = _extract_hook_usages(source); result = codeflash_output # 10.6μs -> 11.1μs (4.95% slower)
def test_hook_pattern_at_word_boundary():
"""Test that hook pattern respects word boundaries."""
source = "myuseState(0) useStateManager(0) useState(0)"
codeflash_output = _extract_hook_usages(source); result = codeflash_output # 10.1μs -> 9.27μs (9.18% faster)
def test_source_with_only_whitespace():
"""Test extraction from source containing only whitespace."""
source = " \n\t \r\n "
codeflash_output = _extract_hook_usages(source); result = codeflash_output # 3.80μs -> 4.05μs (6.18% slower)
def test_hook_with_comment_in_dependencies():
"""Test extraction when source contains comments (naive parser doesn't strip them)."""
source = "useState(0, [value /* important */])"
codeflash_output = _extract_hook_usages(source); result = codeflash_output # 10.3μs -> 10.6μs (3.02% slower)
def test_single_character_hook_name():
"""Test that hook names must have at least 2 characters after 'use'."""
source = "useA()"
codeflash_output = _extract_hook_usages(source); result = codeflash_output # 7.29μs -> 6.29μs (15.9% faster)
def test_very_long_hook_name():
"""Test extraction of a hook with an extremely long name."""
long_hook_name = "use" + "VeryLongCustomHookNameWithManyCharacters" * 5
source = f"{long_hook_name}()"
codeflash_output = _extract_hook_usages(source); result = codeflash_output # 7.75μs -> 6.87μs (12.8% faster)
def test_dependency_array_with_trailing_comma():
"""Test extraction of dependency array with trailing comma."""
source = "useEffect(callback, [dep1, dep2,])"
codeflash_output = _extract_hook_usages(source); result = codeflash_output # 9.93μs -> 10.3μs (3.98% slower)
def test_hook_with_destructured_arguments():
"""Test extraction of hook with destructured arguments."""
source = "useReducer(({ state }, action) => {}, initialState, [init])"
codeflash_output = _extract_hook_usages(source); result = codeflash_output # 11.3μs -> 12.1μs (6.52% slower)
def test_hook_with_spread_operator_in_dependencies():
"""Test extraction when dependency array uses spread operator."""
source = "useMemo(() => {}, [...dependencies])"
codeflash_output = _extract_hook_usages(source); result = codeflash_output # 10.1μs -> 10.5μs (3.35% slower)
def test_hook_without_dependency_array_with_trailing_comma():
"""Test hook call with trailing comma but no dependency array."""
source = "useState(0, callback,)"
codeflash_output = _extract_hook_usages(source); result = codeflash_output # 8.34μs -> 8.02μs (3.99% faster)
def test_many_hooks_in_single_source():
"""Test extraction of 100 hooks from a single source string."""
# Build a source with 100 different hooks
hooks_list = [f"useHook{i}(value, [dep{i}])" for i in range(100)]
source = " ".join(hooks_list)
codeflash_output = _extract_hook_usages(source); result = codeflash_output # 216μs -> 222μs (2.79% slower)
# Verify all hooks were extracted correctly
for i, hook in enumerate(result):
pass
def test_hooks_with_many_dependencies():
"""Test extraction of hooks with very large dependency arrays."""
# Create a dependency array with 100 items
dependencies = ", ".join([f"dep{i}" for i in range(100)])
source = f"useEffect(callback, [{dependencies}])"
codeflash_output = _extract_hook_usages(source); result = codeflash_output # 43.4μs -> 56.7μs (23.5% slower)
def test_deeply_nested_function_calls():
"""Test extraction from source with deeply nested function calls."""
# Create 100 levels of nested function calls
nested = "value"
for i in range(100):
nested = f"func{i}({nested})"
source = f"useState({nested}, [state])"
codeflash_output = _extract_hook_usages(source); result = codeflash_output # 52.1μs -> 67.0μs (22.4% slower)
def test_large_source_with_mixed_content():
"""Test extraction from a large source file with mixed content."""
# Build a large source with hooks, comments, and other code
lines = []
# Add some regular code
for i in range(50):
lines.append(f"const var{i} = {i};")
# Add hooks scattered throughout
for i in range(50):
if i % 2 == 0:
lines.append(f"const hook{i} = useCustomHook{i}(param, [dep{i}]);")
else:
lines.append(f"useEffect{i}(() => {{}}, [dep{i}, other{i}])")
# Add more regular code
for i in range(50):
lines.append(f"function func{i}() {{ return var{i}; }}")
source = "\n".join(lines)
codeflash_output = _extract_hook_usages(source); result = codeflash_output # 164μs -> 176μs (6.57% slower)
def test_hooks_with_very_long_dependency_expressions():
"""Test extraction when dependencies contain very long expressions."""
# Build a long complex dependency
complex_dep = "obj.method().prop.anotherProp.deepMethod().value"
source = f"useMemo(() => {{}}, [{complex_dep}])"
codeflash_output = _extract_hook_usages(source); result = codeflash_output # 12.3μs -> 13.4μs (8.57% slower)
def test_source_with_1000_character_long_line():
"""Test extraction from source with very long lines."""
# Create a single hook with many dependencies
deps = ", ".join([f"dep{i}" for i in range(250)])
source = f"useHook({' ' * 500}[{deps}])"
codeflash_output = _extract_hook_usages(source); result = codeflash_output # 127μs -> 172μs (25.9% slower)
def test_alternating_hooks_and_non_hooks():
"""Test extraction from source alternating between hooks and non-hook calls."""
# Build source with alternating pattern
parts = []
for i in range(100):
if i % 2 == 0:
parts.append(f"useHook{i}(val, [dep])")
else:
parts.append(f"regularFunc{i}(val)")
source = " ".join(parts)
codeflash_output = _extract_hook_usages(source); result = codeflash_output # 114μs -> 112μs (1.37% faster)
# Verify all found items are hooks
for hook in result:
pass
def test_hooks_with_multiline_dependency_arrays():
"""Test extraction when dependency arrays span multiple lines."""
source = """
useEffect(
callback,
[
dep1,
dep2,
dep3,
dep4,
dep5
]
)
"""
codeflash_output = _extract_hook_usages(source); result = codeflash_output # 15.6μs -> 18.5μs (15.7% slower)
def test_performance_with_many_non_matching_patterns():
"""Test performance when source contains many similar but non-matching patterns."""
# Create source with many patterns that almost match but don't
parts = []
for i in range(500):
parts.append(f"notuse{i}()")
parts.append(f"use{i}()") # Lowercase after 'use'
parts.append(f"useHook{i}()") # Actual matching hooks
source = " ".join(parts)
codeflash_output = _extract_hook_usages(source); result = codeflash_output # 806μs -> 515μs (56.3% faster)
for hook in result:
pass
def test_extraction_consistency_with_repeated_source():
"""Test that extraction produces consistent results when run on same source multiple times."""
source = "useState(0, [x, y]) useEffect(() => {}, [z])"
# Run extraction three times
codeflash_output = _extract_hook_usages(source); result1 = codeflash_output # 13.4μs -> 13.3μs (0.609% faster)
codeflash_output = _extract_hook_usages(source); result2 = codeflash_output # 7.36μs -> 7.33μs (0.423% faster)
codeflash_output = _extract_hook_usages(source); result3 = codeflash_output # 5.80μs -> 5.84μs (0.685% slower)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.To test or edit this optimization locally git merge codeflash/optimize-pr1561-2026-02-20T03.21.19
Click to see suggested changes
| for match in hook_pattern.finditer(component_source): | |
| hook_name = match.group(1) | |
| # Try to determine if there's a dependency array | |
| # Look for ], [ pattern after the hook call (simplified heuristic) | |
| rest_of_line = component_source[match.end() :] | |
| has_deps = False | |
| dep_count = 0 | |
| # Simple heuristic: count brackets to find dependency array | |
| bracket_depth = 1 | |
| for i, char in enumerate(rest_of_line): | |
| if char == "(": | |
| bracket_depth += 1 | |
| elif char == ")": | |
| bracket_depth -= 1 | |
| if bracket_depth == 0: | |
| # Check if the last argument before closing paren is an array | |
| preceding = rest_of_line[:i].rstrip() | |
| if preceding.endswith("]"): | |
| has_deps = True | |
| # Count items in the array (rough: count commas + 1 for non-empty) | |
| array_start = preceding.rfind("[") | |
| if array_start >= 0: | |
| array_content = preceding[array_start + 1 : -1].strip() | |
| if array_content: | |
| dep_count = array_content.count(",") + 1 | |
| else: | |
| dep_count = 0 # empty deps [] | |
| has_deps = True | |
| break | |
| s = component_source | |
| s_len = len(s) | |
| for match in hook_pattern.finditer(s): | |
| hook_name = match.group(1) | |
| has_deps = False | |
| dep_count = 0 | |
| # Simple heuristic: count brackets to find dependency array | |
| bracket_depth = 1 | |
| # Use absolute indices to avoid slicing the rest_of_line for large inputs | |
| start_index = match.end() | |
| j = start_index | |
| while j < s_len: | |
| char = s[j] | |
| if char == "(": | |
| bracket_depth += 1 | |
| elif char == ")": | |
| bracket_depth -= 1 | |
| if bracket_depth == 0: | |
| # Check if the last argument before closing paren is an array | |
| # Instead of creating the full 'preceding' slice, scan backwards to skip trailing whitespace | |
| k = j - 1 | |
| while k >= start_index and s[k].isspace(): | |
| k -= 1 | |
| if k >= start_index and s[k] == "]": | |
| has_deps = True | |
| # Find the corresponding '[' within the bounds to get the array content | |
| array_start = s.rfind("[", start_index, k + 1) | |
| if array_start >= 0: | |
| # Extract array content between '[' and ']' and trim | |
| array_content = s[array_start + 1 : k].strip() | |
| if array_content: | |
| dep_count = array_content.count(",") + 1 | |
| else: | |
| dep_count = 0 # empty deps [] | |
| has_deps = True | |
| break | |
| j += 1 | |
⚡️ Codeflash found optimizations for this PR📄 53% (0.53x) speedup for
|
| # Ensure user-event import if user interactions are tested | ||
| if ( | ||
| "click" in result.lower() or "type" in result.lower() or "userEvent" in result | ||
| ) and "@testing-library/user-event" not in result: |
There was a problem hiding this comment.
Bug: "type" in result.lower() will match virtually every TypeScript test file (type annotations, componentType, etc.), causing a spurious userEvent import to be added to most generated tests. Consider matching on more specific patterns like userEvent.click or userEvent.type instead of bare keyword matching.
| ) and "@testing-library/user-event" not in result: | |
| if "userevent.click" in result.lower() or "userevent.type" in result.lower(): |
| return self.original_avg_duration_ms / self.optimized_avg_duration_ms | ||
|
|
||
|
|
||
| def compare_render_benchmarks( |
There was a problem hiding this comment.
Bug: When the optimized duration is 0 (best possible outcome - render time eliminated entirely), this returns 0.0 instead of a high value. This means format_render_benchmark_for_pr won't display the speedup (benchmark.render_speedup_x > 1 is False), even though the optimization completely eliminated render time.
| def compare_render_benchmarks( | |
| if self.optimized_avg_duration_ms == 0: | |
| return float("inf") if self.original_avg_duration_ms > 0 else 1.0 |
| result = source | ||
| # Process in reverse order by start_line to preserve positions | ||
| for comp in sorted(components, key=lambda c: c.start_line, reverse=True): | ||
| if comp.returns_jsx: |
There was a problem hiding this comment.
Bug: Each call to instrument_component_with_profiler re-parses the source and inserts render counter code after imports via _insert_after_imports. After the first component is instrumented, the source is modified (code prepended at top), making all subsequent byte offsets from the original find_react_components stale. Processing in reverse by start_line helps for within-function edits, but _insert_after_imports shifts everything, so the second+ components will have profiler wrappers at wrong positions.
Consider collecting all components, then doing a single-pass instrumentation, or re-parsing after each component.
| # Aggregate original metrics | ||
| orig_count = max((p.render_count for p in original_profiles), default=0) | ||
| orig_durations = [p.actual_duration_ms for p in original_profiles] | ||
| orig_avg_duration = sum(orig_durations) / len(orig_durations) if orig_durations else 0.0 | ||
|
|
||
| # Aggregate optimized metrics | ||
| opt_count = max((p.render_count for p in optimized_profiles), default=0) | ||
| opt_durations = [p.actual_duration_ms for p in optimized_profiles] | ||
| opt_avg_duration = sum(opt_durations) / len(opt_durations) if opt_durations else 0.0 | ||
|
|
There was a problem hiding this comment.
⚡️Codeflash found 57% (0.57x) speedup for compare_render_benchmarks in codeflash/languages/javascript/frameworks/react/benchmarking.py
⏱️ Runtime : 979 microseconds → 624 microseconds (best of 140 runs)
📝 Explanation and details
The optimized code achieves a 56% runtime improvement by replacing multiple passes over the profile lists with single-pass aggregation.
Key Changes:
-
Eliminated intermediate list creation: The original code created temporary lists (
orig_durations,opt_durations) and then calledsum()on them, requiring two full passes through each profile list. -
Single-pass aggregation: The optimized version processes each profile list once, accumulating the total duration and tracking the maximum render count simultaneously in a single loop.
-
Reduced built-in overhead: Avoided the overhead of the
max()built-in with generator expressions, which had to iterate through all profiles to find the maximum. The optimized code tracks the maximum during the single pass with simple comparison operations.
Performance Impact:
The line profiler shows the optimization particularly benefits workloads with many profiles:
- For 1000+ profile lists: ~31-46% faster
- For smaller lists (1-10 profiles): ~90-110% faster
- The improvement scales with list size as it avoids the overhead of multiple iterations
Why It's Faster:
In Python, list comprehensions and generator expressions with max()/sum() each require a full traversal of the input data. By combining max-finding and sum calculation into a single loop, we:
- Reduce the number of Python bytecode operations
- Improve cache locality by processing each profile object once
- Eliminate temporary list allocations
Context from References:
The function is called in integration tests processing React component render profiles. Given it processes aggregated profiling data that could contain hundreds of render events per component, the single-pass optimization provides meaningful speedup in the benchmarking pipeline where these comparisons run repeatedly during optimization workflows.
✅ Correctness verification report:
| Test | Status |
|---|---|
| ⚙️ Existing Unit Tests | 🔘 None Found |
| 🌀 Generated Regression Tests | ✅ 139 Passed |
| ⏪ Replay Tests | 🔘 None Found |
| 🔎 Concolic Coverage Tests | 🔘 None Found |
| 📊 Tests Coverage | 100.0% |
🌀 Click to see Generated Regression Tests
import pytest # used for our unit tests
from codeflash.languages.javascript.frameworks.react.benchmarking import (
RenderBenchmark, compare_render_benchmarks)
# import the real classes and function under test from the package
from codeflash.languages.javascript.parse import RenderProfile
def test_empty_lists_return_none():
# If either input list is empty the function should return None
# Prepare an example profile to use in one of the parameters
rp = RenderProfile("Button", "mount", 12.0, 10.0, 1)
# original empty
codeflash_output = compare_render_benchmarks([], [rp]); res = codeflash_output # 410ns -> 431ns (4.87% slower)
# optimized empty
codeflash_output = compare_render_benchmarks([rp], []); res = codeflash_output # 300ns -> 290ns (3.45% faster)
# both empty
codeflash_output = compare_render_benchmarks([], []); res = codeflash_output # 170ns -> 170ns (0.000% faster)
def test_single_profile_each_basic_aggregation():
# Simple case: one original and one optimized profile
orig = RenderProfile("MyComponent", "mount", 25.5, 20.0, 2)
opt = RenderProfile("MyComponent", "update", 10.0, 9.0, 1)
codeflash_output = compare_render_benchmarks([orig], [opt]); bench = codeflash_output # 6.04μs -> 2.96μs (104% faster)
def test_multiple_profiles_aggregation_and_component_name_choice():
# Multiple original profiles: check max(render_count) and average(actual_duration_ms)
originals = [
RenderProfile("Primary", "mount", 10.0, 9.0, 1),
RenderProfile("Primary-ALT", "update", 30.0, 28.0, 3),
RenderProfile("Primary", "update", 20.0, 19.0, 2),
]
# Multiple optimized profiles with different component names to ensure original[0] wins
optimized = [
RenderProfile("Other", "update", 5.0, 5.0, 2),
RenderProfile("Other", "update", 15.0, 14.0, 4),
]
codeflash_output = compare_render_benchmarks(originals, optimized); bench = codeflash_output # 6.22μs -> 3.07μs (103% faster)
def test_zero_and_negative_durations_and_counts():
# Ensure function handles zero and negative durations gracefully (mathematical average)
originals = [
RenderProfile("EdgeCase", "mount", 0.0, 0.0, 0),
RenderProfile("EdgeCase", "update", -5.0, -5.0, 0),
]
optimized = [
RenderProfile("EdgeCase", "mount", 0.0, 0.0, 0),
]
codeflash_output = compare_render_benchmarks(originals, optimized); bench = codeflash_output # 5.70μs -> 2.81μs (103% faster)
def test_phase_values_do_not_affect_aggregation():
# Different phase strings should not affect aggregation logic
originals = [
RenderProfile("PhaseComp", "mount", 2.0, 1.0, 1),
RenderProfile("PhaseComp", "update", 8.0, 7.0, 2),
]
optimized = [
RenderProfile("PhaseComp", "mount", 5.0, 4.0, 1),
RenderProfile("PhaseComp", "update", 7.0, 6.0, 3),
]
codeflash_output = compare_render_benchmarks(originals, optimized); bench = codeflash_output # 5.80μs -> 2.82μs (106% faster)
def test_component_name_with_special_characters():
# Component names containing punctuation and whitespace are preserved
name = "Comp v2.0 - test_case/α" # includes punctuation and non-ascii character
originals = [RenderProfile(name, "mount", 3.0, 3.0, 1)]
optimized = [RenderProfile(name, "update", 1.0, 1.0, 1)]
codeflash_output = compare_render_benchmarks(originals, optimized); bench = codeflash_output # 5.40μs -> 2.57μs (110% faster)
def test_large_scale_aggregation_1000_elements_original_and_optimized():
# Create 1000 original profiles with durations 1..1000 and render_count equal to the index
originals = [
RenderProfile("BigComp", "update", float(i), 0.0, i) for i in range(1, 1001)
]
# Create 1000 optimized profiles with durations 1001..2000 and render_count equal to index+1000
optimized = [
RenderProfile("BigComp", "update", float(1000 + i), 0.0, 1000 + i)
for i in range(1, 1001)
]
codeflash_output = compare_render_benchmarks(originals, optimized); bench = codeflash_output # 128μs -> 108μs (19.0% faster)
def test_large_scale_with_repeated_values_and_max_check():
# 1000 items where render_count cycles but we ensure a known max exists
# Use render_count = i % 37 and include one item with a much larger count to set the max
originals = [
RenderProfile("CycleComp", "mount", float(i % 10), 0.0, i % 37) for i in range(1000)
]
# Insert one outlier with very large render_count at the beginning (to also test component_name)
originals.insert(0, RenderProfile("CycleComp", "mount", 0.0, 0.0, 9999))
optimized = [
RenderProfile("CycleComp", "update", 5.0, 0.0, 0) for _ in range(1000)
]
codeflash_output = compare_render_benchmarks(originals, optimized); bench = codeflash_output # 126μs -> 86.8μs (46.0% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.import pytest
from codeflash.languages.javascript.frameworks.react.benchmarking import (
RenderBenchmark, compare_render_benchmarks)
from codeflash.languages.javascript.parse import RenderProfile
def test_compare_render_benchmarks_basic_single_profile_each():
"""Test basic comparison with one profile in each list."""
original = [
RenderProfile(
component_name="Button",
phase="mount",
actual_duration_ms=5.0,
base_duration_ms=3.0,
render_count=1,
)
]
optimized = [
RenderProfile(
component_name="Button",
phase="mount",
actual_duration_ms=3.0,
base_duration_ms=2.0,
render_count=1,
)
]
codeflash_output = compare_render_benchmarks(original, optimized); result = codeflash_output # 5.59μs -> 2.75μs (103% faster)
def test_compare_render_benchmarks_multiple_profiles_both_lists():
"""Test comparison with multiple profiles in each list."""
original = [
RenderProfile(
component_name="Modal",
phase="mount",
actual_duration_ms=10.0,
base_duration_ms=8.0,
render_count=2,
),
RenderProfile(
component_name="Modal",
phase="update",
actual_duration_ms=8.0,
base_duration_ms=6.0,
render_count=3,
),
]
optimized = [
RenderProfile(
component_name="Modal",
phase="mount",
actual_duration_ms=6.0,
base_duration_ms=5.0,
render_count=2,
),
RenderProfile(
component_name="Modal",
phase="update",
actual_duration_ms=4.0,
base_duration_ms=3.0,
render_count=2,
),
]
codeflash_output = compare_render_benchmarks(original, optimized); result = codeflash_output # 5.91μs -> 2.87μs (106% faster)
def test_compare_render_benchmarks_uses_first_component_name():
"""Test that the function uses the first profile's component name."""
original = [
RenderProfile(
component_name="Component1",
phase="mount",
actual_duration_ms=5.0,
base_duration_ms=4.0,
render_count=1,
)
]
optimized = [
RenderProfile(
component_name="Component1",
phase="mount",
actual_duration_ms=3.0,
base_duration_ms=2.0,
render_count=1,
)
]
codeflash_output = compare_render_benchmarks(original, optimized); result = codeflash_output # 5.30μs -> 2.69μs (96.7% faster)
def test_compare_render_benchmarks_returns_render_benchmark_type():
"""Test that the return type is RenderBenchmark."""
original = [
RenderProfile(
component_name="Test",
phase="mount",
actual_duration_ms=1.0,
base_duration_ms=1.0,
render_count=1,
)
]
optimized = [
RenderProfile(
component_name="Test",
phase="mount",
actual_duration_ms=1.0,
base_duration_ms=1.0,
render_count=1,
)
]
codeflash_output = compare_render_benchmarks(original, optimized); result = codeflash_output # 5.24μs -> 2.63μs (98.9% faster)
def test_compare_render_benchmarks_zero_duration_values():
"""Test with zero duration values."""
original = [
RenderProfile(
component_name="Fast",
phase="mount",
actual_duration_ms=0.0,
base_duration_ms=0.0,
render_count=1,
)
]
optimized = [
RenderProfile(
component_name="Fast",
phase="mount",
actual_duration_ms=0.0,
base_duration_ms=0.0,
render_count=1,
)
]
codeflash_output = compare_render_benchmarks(original, optimized); result = codeflash_output # 5.21μs -> 2.57μs (102% faster)
def test_compare_render_benchmarks_very_small_float_values():
"""Test with very small floating point values."""
original = [
RenderProfile(
component_name="Micro",
phase="mount",
actual_duration_ms=0.001,
base_duration_ms=0.001,
render_count=1,
)
]
optimized = [
RenderProfile(
component_name="Micro",
phase="mount",
actual_duration_ms=0.0005,
base_duration_ms=0.0005,
render_count=1,
)
]
codeflash_output = compare_render_benchmarks(original, optimized); result = codeflash_output # 5.28μs -> 2.60μs (103% faster)
def test_compare_render_benchmarks_large_duration_values():
"""Test with very large duration values."""
original = [
RenderProfile(
component_name="Slow",
phase="mount",
actual_duration_ms=10000.5,
base_duration_ms=9999.5,
render_count=1,
)
]
optimized = [
RenderProfile(
component_name="Slow",
phase="mount",
actual_duration_ms=5000.25,
base_duration_ms=4999.75,
render_count=1,
)
]
codeflash_output = compare_render_benchmarks(original, optimized); result = codeflash_output # 5.22μs -> 2.53μs (106% faster)
def test_compare_render_benchmarks_high_render_count():
"""Test with high render counts."""
original = [
RenderProfile(
component_name="Expensive",
phase="mount",
actual_duration_ms=2.0,
base_duration_ms=1.0,
render_count=1000,
)
]
optimized = [
RenderProfile(
component_name="Expensive",
phase="mount",
actual_duration_ms=1.0,
base_duration_ms=0.5,
render_count=500,
)
]
codeflash_output = compare_render_benchmarks(original, optimized); result = codeflash_output # 5.27μs -> 2.60μs (102% faster)
def test_compare_render_benchmarks_empty_original_list_returns_none():
"""Test that empty original_profiles list returns None."""
original = []
optimized = [
RenderProfile(
component_name="Button",
phase="mount",
actual_duration_ms=3.0,
base_duration_ms=2.0,
render_count=1,
)
]
codeflash_output = compare_render_benchmarks(original, optimized); result = codeflash_output # 431ns -> 411ns (4.87% faster)
def test_compare_render_benchmarks_empty_optimized_list_returns_none():
"""Test that empty optimized_profiles list returns None."""
original = [
RenderProfile(
component_name="Button",
phase="mount",
actual_duration_ms=5.0,
base_duration_ms=3.0,
render_count=1,
)
]
optimized = []
codeflash_output = compare_render_benchmarks(original, optimized); result = codeflash_output # 421ns -> 441ns (4.54% slower)
def test_compare_render_benchmarks_both_lists_empty_returns_none():
"""Test that both lists being empty returns None."""
original = []
optimized = []
codeflash_output = compare_render_benchmarks(original, optimized); result = codeflash_output # 411ns -> 400ns (2.75% faster)
def test_compare_render_benchmarks_special_characters_in_component_name():
"""Test with special characters in component name."""
original = [
RenderProfile(
component_name="Button-Primary_V2.0",
phase="mount",
actual_duration_ms=5.0,
base_duration_ms=4.0,
render_count=1,
)
]
optimized = [
RenderProfile(
component_name="Button-Primary_V2.0",
phase="mount",
actual_duration_ms=3.0,
base_duration_ms=2.0,
render_count=1,
)
]
codeflash_output = compare_render_benchmarks(original, optimized); result = codeflash_output # 5.64μs -> 2.77μs (104% faster)
def test_compare_render_benchmarks_unicode_component_name():
"""Test with unicode characters in component name."""
original = [
RenderProfile(
component_name="按钮",
phase="mount",
actual_duration_ms=5.0,
base_duration_ms=4.0,
render_count=1,
)
]
optimized = [
RenderProfile(
component_name="按钮",
phase="mount",
actual_duration_ms=3.0,
base_duration_ms=2.0,
render_count=1,
)
]
codeflash_output = compare_render_benchmarks(original, optimized); result = codeflash_output # 5.32μs -> 2.60μs (104% faster)
def test_compare_render_benchmarks_render_count_zero():
"""Test with zero render count."""
original = [
RenderProfile(
component_name="Test",
phase="mount",
actual_duration_ms=5.0,
base_duration_ms=4.0,
render_count=0,
)
]
optimized = [
RenderProfile(
component_name="Test",
phase="mount",
actual_duration_ms=3.0,
base_duration_ms=2.0,
render_count=0,
)
]
codeflash_output = compare_render_benchmarks(original, optimized); result = codeflash_output # 5.25μs -> 2.60μs (102% faster)
def test_compare_render_benchmarks_negative_duration_values():
"""Test with negative duration values (edge case, though unlikely in real use)."""
original = [
RenderProfile(
component_name="Negative",
phase="mount",
actual_duration_ms=-1.0,
base_duration_ms=-1.0,
render_count=1,
)
]
optimized = [
RenderProfile(
component_name="Negative",
phase="mount",
actual_duration_ms=-0.5,
base_duration_ms=-0.5,
render_count=1,
)
]
codeflash_output = compare_render_benchmarks(original, optimized); result = codeflash_output # 5.24μs -> 2.62μs (99.6% faster)
def test_compare_render_benchmarks_max_render_count_used():
"""Test that max render_count is correctly selected."""
original = [
RenderProfile(
component_name="MaxTest",
phase="mount",
actual_duration_ms=5.0,
base_duration_ms=4.0,
render_count=10,
),
RenderProfile(
component_name="MaxTest",
phase="update",
actual_duration_ms=3.0,
base_duration_ms=2.0,
render_count=50,
),
RenderProfile(
component_name="MaxTest",
phase="update",
actual_duration_ms=2.0,
base_duration_ms=1.0,
render_count=30,
),
]
optimized = [
RenderProfile(
component_name="MaxTest",
phase="mount",
actual_duration_ms=2.0,
base_duration_ms=1.0,
render_count=5,
)
]
codeflash_output = compare_render_benchmarks(original, optimized); result = codeflash_output # 5.75μs -> 2.90μs (98.0% faster)
def test_compare_render_benchmarks_average_duration_calculation():
"""Test that average duration is correctly calculated."""
original = [
RenderProfile(
component_name="Avg",
phase="mount",
actual_duration_ms=10.0,
base_duration_ms=8.0,
render_count=1,
),
RenderProfile(
component_name="Avg",
phase="update",
actual_duration_ms=20.0,
base_duration_ms=16.0,
render_count=2,
),
RenderProfile(
component_name="Avg",
phase="update",
actual_duration_ms=30.0,
base_duration_ms=24.0,
render_count=3,
),
]
optimized = [
RenderProfile(
component_name="Avg",
phase="mount",
actual_duration_ms=5.0,
base_duration_ms=4.0,
render_count=1,
),
RenderProfile(
component_name="Avg",
phase="update",
actual_duration_ms=10.0,
base_duration_ms=8.0,
render_count=2,
),
]
codeflash_output = compare_render_benchmarks(original, optimized); result = codeflash_output # 5.78μs -> 2.87μs (102% faster)
def test_compare_render_benchmarks_different_phases_handled():
"""Test that different phase values are handled correctly."""
original = [
RenderProfile(
component_name="Component",
phase="mount",
actual_duration_ms=5.0,
base_duration_ms=4.0,
render_count=1,
),
RenderProfile(
component_name="Component",
phase="update",
actual_duration_ms=3.0,
base_duration_ms=2.0,
render_count=1,
),
]
optimized = [
RenderProfile(
component_name="Component",
phase="mount",
actual_duration_ms=2.0,
base_duration_ms=1.0,
render_count=1,
),
RenderProfile(
component_name="Component",
phase="update",
actual_duration_ms=1.0,
base_duration_ms=0.5,
render_count=1,
),
]
codeflash_output = compare_render_benchmarks(original, optimized); result = codeflash_output # 5.68μs -> 2.79μs (104% faster)
def test_compare_render_benchmarks_single_very_long_list_original():
"""Test with many profiles in original list."""
original = [
RenderProfile(
component_name="LongList",
phase="mount" if i % 2 == 0 else "update",
actual_duration_ms=float(i),
base_duration_ms=float(i - 1),
render_count=i + 1,
)
for i in range(1, 11)
]
optimized = [
RenderProfile(
component_name="LongList",
phase="mount",
actual_duration_ms=1.0,
base_duration_ms=1.0,
render_count=1,
)
]
codeflash_output = compare_render_benchmarks(original, optimized); result = codeflash_output # 6.16μs -> 3.24μs (90.4% faster)
# Average: (1 + 2 + 3 + ... + 10) / 10 = 5.5
expected_avg = sum(range(1, 11)) / 10
def test_compare_render_benchmarks_three_profiles_average():
"""Test average calculation with exactly three profiles."""
original = [
RenderProfile(
component_name="Three",
phase="mount",
actual_duration_ms=9.0,
base_duration_ms=8.0,
render_count=1,
),
RenderProfile(
component_name="Three",
phase="update",
actual_duration_ms=12.0,
base_duration_ms=11.0,
render_count=2,
),
RenderProfile(
component_name="Three",
phase="update",
actual_duration_ms=15.0,
base_duration_ms=14.0,
render_count=3,
),
]
optimized = [
RenderProfile(
component_name="Three",
phase="mount",
actual_duration_ms=3.0,
base_duration_ms=2.0,
render_count=1,
),
RenderProfile(
component_name="Three",
phase="update",
actual_duration_ms=6.0,
base_duration_ms=5.0,
render_count=2,
),
RenderProfile(
component_name="Three",
phase="update",
actual_duration_ms=9.0,
base_duration_ms=8.0,
render_count=3,
),
]
codeflash_output = compare_render_benchmarks(original, optimized); result = codeflash_output # 5.69μs -> 2.96μs (92.6% faster)
def test_compare_render_benchmarks_many_profiles_original():
"""Test with 100 profiles in original list."""
original = [
RenderProfile(
component_name="LargeComponent",
phase="mount" if i % 2 == 0 else "update",
actual_duration_ms=float(i % 50 + 1),
base_duration_ms=float((i % 50)),
render_count=(i % 100) + 1,
)
for i in range(100)
]
optimized = [
RenderProfile(
component_name="LargeComponent",
phase="mount",
actual_duration_ms=5.0,
base_duration_ms=4.0,
render_count=10,
)
]
codeflash_output = compare_render_benchmarks(original, optimized); result = codeflash_output # 12.6μs -> 8.00μs (57.4% faster)
# Verify that the aggregation works correctly
expected_max_render = max((i % 100) + 1 for i in range(100))
def test_compare_render_benchmarks_many_profiles_optimized():
"""Test with 100 profiles in optimized list."""
original = [
RenderProfile(
component_name="OptimizedComponent",
phase="mount",
actual_duration_ms=10.0,
base_duration_ms=8.0,
render_count=50,
)
]
optimized = [
RenderProfile(
component_name="OptimizedComponent",
phase="mount" if i % 2 == 0 else "update",
actual_duration_ms=float(i % 30 + 1),
base_duration_ms=float((i % 30)),
render_count=(i % 80) + 1,
)
for i in range(100)
]
codeflash_output = compare_render_benchmarks(original, optimized); result = codeflash_output # 12.3μs -> 7.86μs (56.2% faster)
# Verify that the aggregation works correctly
expected_max_render = max((i % 80) + 1 for i in range(100))
def test_compare_render_benchmarks_both_lists_many_profiles():
"""Test with 100 profiles in both lists."""
original = [
RenderProfile(
component_name="DoubleScale",
phase="mount" if i % 2 == 0 else "update",
actual_duration_ms=float(i % 40 + 2),
base_duration_ms=float((i % 40) + 1),
render_count=(i % 120) + 1,
)
for i in range(100)
]
optimized = [
RenderProfile(
component_name="DoubleScale",
phase="mount" if i % 2 == 0 else "update",
actual_duration_ms=float(i % 20 + 1),
base_duration_ms=float((i % 20)),
render_count=(i % 60) + 1,
)
for i in range(100)
]
codeflash_output = compare_render_benchmarks(original, optimized); result = codeflash_output # 18.6μs -> 12.8μs (45.2% faster)
# Verify maximum render counts
original_max = max((i % 120) + 1 for i in range(100))
optimized_max = max((i % 60) + 1 for i in range(100))
def test_compare_render_benchmarks_1000_profiles_original():
"""Test with 1000 profiles in original list for scalability."""
original = [
RenderProfile(
component_name="Scalable",
phase="mount" if i % 2 == 0 else "update",
actual_duration_ms=float((i % 100) + 1),
base_duration_ms=float((i % 100)),
render_count=(i % 500) + 1,
)
for i in range(1000)
]
optimized = [
RenderProfile(
component_name="Scalable",
phase="mount",
actual_duration_ms=50.0,
base_duration_ms=49.0,
render_count=250,
)
]
codeflash_output = compare_render_benchmarks(original, optimized); result = codeflash_output # 66.9μs -> 51.0μs (31.3% faster)
def test_compare_render_benchmarks_1000_profiles_optimized():
"""Test with 1000 profiles in optimized list for scalability."""
original = [
RenderProfile(
component_name="ScalableOpt",
phase="mount",
actual_duration_ms=100.0,
base_duration_ms=98.0,
render_count=500,
)
]
optimized = [
RenderProfile(
component_name="ScalableOpt",
phase="mount" if i % 2 == 0 else "update",
actual_duration_ms=float((i % 50) + 1),
base_duration_ms=float((i % 50)),
render_count=(i % 250) + 1,
)
for i in range(1000)
]
codeflash_output = compare_render_benchmarks(original, optimized); result = codeflash_output # 66.2μs -> 47.3μs (40.0% faster)
def test_compare_render_benchmarks_1000_profiles_both_lists():
"""Test with 1000 profiles in both lists for scalability."""
original = [
RenderProfile(
component_name="FullScale",
phase="mount" if i % 2 == 0 else "update",
actual_duration_ms=float((i % 75) + 3),
base_duration_ms=float((i % 75) + 2),
render_count=(i % 300) + 1,
)
for i in range(1000)
]
optimized = [
RenderProfile(
component_name="FullScale",
phase="mount" if i % 2 == 0 else "update",
actual_duration_ms=float((i % 40) + 1),
base_duration_ms=float((i % 40)),
render_count=(i % 150) + 1,
)
for i in range(1000)
]
codeflash_output = compare_render_benchmarks(original, optimized); result = codeflash_output # 126μs -> 92.0μs (37.6% faster)
def test_compare_render_benchmarks_identical_profiles():
"""Test comparison when original and optimized are identical."""
profiles = [
RenderProfile(
component_name="Identical",
phase="mount",
actual_duration_ms=7.5,
base_duration_ms=6.5,
render_count=5,
),
RenderProfile(
component_name="Identical",
phase="update",
actual_duration_ms=4.5,
base_duration_ms=3.5,
render_count=10,
),
]
codeflash_output = compare_render_benchmarks(profiles, profiles); result = codeflash_output # 6.00μs -> 2.90μs (107% faster)
def test_compare_render_benchmarks_precision_float_arithmetic():
"""Test floating point precision in average calculations."""
original = [
RenderProfile(
component_name="Precision",
phase="mount",
actual_duration_ms=1.1,
base_duration_ms=1.0,
render_count=1,
),
RenderProfile(
component_name="Precision",
phase="update",
actual_duration_ms=2.2,
base_duration_ms=2.0,
render_count=2,
),
RenderProfile(
component_name="Precision",
phase="update",
actual_duration_ms=3.3,
base_duration_ms=3.0,
render_count=3,
),
]
optimized = [
RenderProfile(
component_name="Precision",
phase="mount",
actual_duration_ms=0.5,
base_duration_ms=0.4,
render_count=1,
),
RenderProfile(
component_name="Precision",
phase="update",
actual_duration_ms=0.6,
base_duration_ms=0.5,
render_count=2,
),
]
codeflash_output = compare_render_benchmarks(original, optimized); result = codeflash_output # 5.79μs -> 2.75μs (110% faster)
def test_compare_render_benchmarks_100_identical_profiles():
"""Test with 100 identical profiles in both lists."""
profiles = [
RenderProfile(
component_name="Uniform",
phase="mount",
actual_duration_ms=5.0,
base_duration_ms=4.0,
render_count=10,
)
for _ in range(100)
]
codeflash_output = compare_render_benchmarks(profiles, profiles); result = codeflash_output # 18.3μs -> 10.7μs (70.9% faster)
def test_compare_render_benchmarks_performance_many_iterations():
"""Test performance by creating many different component comparisons."""
for iteration in range(100):
original = [
RenderProfile(
component_name=f"Component_{iteration}",
phase="mount",
actual_duration_ms=5.0 + iteration,
base_duration_ms=4.0 + iteration,
render_count=10 + iteration,
)
]
optimized = [
RenderProfile(
component_name=f"Component_{iteration}",
phase="mount",
actual_duration_ms=3.0 + iteration,
base_duration_ms=2.0 + iteration,
render_count=5 + iteration,
)
]
codeflash_output = compare_render_benchmarks(original, optimized); result = codeflash_output # 265μs -> 131μs (103% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.To test or edit this optimization locally git merge codeflash/optimize-pr1561-2026-02-20T03.48.17
Click to see suggested changes
| # Aggregate original metrics | |
| orig_count = max((p.render_count for p in original_profiles), default=0) | |
| orig_durations = [p.actual_duration_ms for p in original_profiles] | |
| orig_avg_duration = sum(orig_durations) / len(orig_durations) if orig_durations else 0.0 | |
| # Aggregate optimized metrics | |
| opt_count = max((p.render_count for p in optimized_profiles), default=0) | |
| opt_durations = [p.actual_duration_ms for p in optimized_profiles] | |
| opt_avg_duration = sum(opt_durations) / len(opt_durations) if opt_durations else 0.0 | |
| # Aggregate original metrics in a single pass | |
| orig_count = 0 | |
| orig_total_duration = 0.0 | |
| orig_len = len(original_profiles) | |
| for p in original_profiles: | |
| orig_total_duration += p.actual_duration_ms | |
| if p.render_count > orig_count: | |
| orig_count = p.render_count | |
| orig_avg_duration = orig_total_duration / orig_len if orig_len else 0.0 | |
| # Aggregate optimized metrics in a single pass | |
| opt_count = 0 | |
| opt_total_duration = 0.0 | |
| opt_len = len(optimized_profiles) | |
| for p in optimized_profiles: | |
| opt_total_duration += p.actual_duration_ms | |
| if p.render_count > opt_count: | |
| opt_count = p.render_count | |
| opt_avg_duration = opt_total_duration / opt_len if opt_len else 0.0 | |
This optimization achieves a **12% runtime improvement** (from 3.47ms to 3.08ms) by eliminating redundant work in React component analysis. The key changes deliver measurable performance gains: ## Primary Optimizations **1. Module-level Regex Compilation** The original code recompiled three regular expressions on every function call: - `_extract_hook_usages`: compiled `_HOOK_PATTERN` on each invocation - `_extract_child_components`: compiled `_JSX_COMPONENT_RE` on each invocation - `_extract_context_subscriptions`: compiled `_CONTEXT_RE` on each invocation Moving these to module-level constants (`_HOOK_PATTERN`, `_JSX_COMPONENT_RE`, `_CONTEXT_RE`) eliminates this overhead. Line profiler data shows this saves ~25ms per call to `_extract_hook_usages` and similar savings in other functions (e.g., `_extract_child_components` dropped from 1.48ms to 1.10ms). **2. Index-based Iteration in `_extract_hook_usages`** The original code created a new substring `rest_of_line = component_source[match.end():]` for every hook match, then performed string operations on it. With 672 hooks detected in the test workload, this created 672 unnecessary string allocations. The optimized version iterates by index through the original string (`while j < n: char = cs[j]`), avoiding substring creation. This reduces string slicing operations and memory allocations, contributing to the overall runtime improvement. **3. Eliminated Repeated Import** Removed `import re` statements from function bodies, preventing import overhead on every call (though Python caches imports, the lookup still has cost). ## Performance Impact by Test Case The optimization shows consistent improvements across workloads: - **Small components**: 9-47% faster (basic hook extraction, empty source) - **Medium complexity**: 7-12% faster (multiple hooks, optimization detection) - **Large scale (500+ elements)**: 12.5% faster - demonstrates excellent scaling as the regex compilation savings compound with more matches ## Workload Context Based on `function_references`, this code runs in an integration test that analyzes real React components (e.g., TaskList.tsx). The function extracts hooks, child components, and optimization opportunities - operations that can be called repeatedly during codebase analysis. The 12% runtime improvement means faster analysis cycles when processing multiple components or large codebases. The optimization particularly benefits scenarios with: - Many hook calls per component (common in modern React) - Multiple component analyses in sequence (the module-level regex stays compiled) - Large component source files (index-based iteration avoids O(n²) substring creation)
⚡️ Codeflash found optimizations for this PR📄 12% (0.12x) speedup for
|
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
⚡️ Codeflash found optimizations for this PR📄 3,021% (30.21x) speedup for
|
…2026-02-20T03.56.09 ⚡️ Speed up function `extract_react_context` by 12% in PR #1561 (`add/support_react`)
⚡️ Codeflash found optimizations for this PR📄 12% (0.12x) speedup for
|
⚡️ Codeflash found optimizations for this PR📄 203% (2.03x) speedup for
|
The optimized code achieves an **87% speedup** (from 85.3ms to 45.6ms) through two primary performance improvements in the `_build_runtime_map` method:
## Key Optimizations
**1. Path Resolution Caching (Primary Improvement)**
The original code called `resolve_js_test_module_path()` and `abs_path.resolve().with_suffix("")` for every invocation, even when multiple invocations shared the same `test_module_path`. The optimized version introduces `_resolved_path_cache` to store computed path strings per module path, eliminating redundant filesystem operations.
Line profiler data confirms the dramatic impact:
- `resolve_js_test_module_path` calls: 3,481 → 1,480 (57% reduction)
- Time in path resolution: 84.7ms → 38.6ms (54% faster)
- Time in `abs_path.resolve()`: 186.9ms → 89.2ms (52% faster)
**2. Optimized String Parsing**
The original code parsed `iteration_id` inefficiently:
```python
parts = iteration_id.split("_").__len__() # Creates list, calls __len__()
cur_invid = iteration_id.split("_")[0] if parts < 3 else "_".join(iteration_id.split("_")[:-1]) # Splits again!
```
The optimized version splits once and reuses the result:
```python
parts = iteration_id.split("_")
parts_len = len(parts)
cur_invid = parts[0] if parts_len < 3 else "_".join(parts[:-1])
```
Additionally, dictionary access was optimized from:
```python
if match_key not in unique_inv_ids:
unique_inv_ids[match_key] = 0
unique_inv_ids[match_key] += min(runtimes)
```
to:
```python
unique_inv_ids[match_key] = unique_inv_ids.get(match_key, 0) + min(runtimes)
```
## Performance Benefits by Test Type
The optimization particularly excels with workloads featuring:
1. **Many invocations with shared module paths** (e.g., `test_large_number_of_invocations`: 1567% faster, `test_many_different_iteration_ids`: 3037% faster) - the cache eliminates redundant path resolutions
2. **Repeated path resolution** (e.g., `test_multiple_invocations_same_module`: 52.4% faster) - cache hits avoid expensive filesystem operations
3. **Complex iteration IDs** (e.g., `test_complex_iteration_id_patterns`: 2472% faster) - optimized string parsing reduces per-item overhead
The optimization maintains correctness across all test cases while delivering substantial performance improvements, especially in realistic scenarios where test suites contain multiple tests in the same modules.
⚡️ Codeflash found optimizations for this PR📄 87% (0.87x) speedup for
|
| name = self.get_node_text(child, source_bytes) | ||
| names.append((name, None)) | ||
| elif child.type == "pair_pattern": | ||
| # { a: aliasA } - renamed import | ||
| key_node = child.child_by_field_name("key") | ||
| value_node = child.child_by_field_name("value") | ||
| if key_node and value_node: | ||
| original_name = self.get_node_text(key_node, source_bytes) | ||
| alias = self.get_node_text(value_node, source_bytes) |
There was a problem hiding this comment.
⚡️Codeflash found 16% (0.16x) speedup for TreeSitterAnalyzer._extract_object_pattern_names in codeflash/languages/javascript/treesitter_utils.py
⏱️ Runtime : 392 microseconds → 338 microseconds (best of 9 runs)
📝 Explanation and details
The optimization achieves a 15% runtime improvement (392μs → 338μs) by eliminating function call overhead in a performance-critical loop within _extract_object_pattern_names.
What Changed:
Instead of calling self.get_node_text() three times per iteration to extract text from nodes, the optimized version directly performs the byte slicing and UTF-8 decoding inline:
self.get_node_text(child, source_bytes)→source_bytes[child.start_byte : child.end_byte].decode("utf8")
Why This Is Faster:
-
Eliminates function call overhead: Python function calls involve stack frame creation, argument passing, and attribute lookups. With 506 shorthand patterns and 502 pair patterns (1,004 key nodes + 502 value nodes = ~1,506 total extractions per test), removing these calls saves significant overhead.
-
Reduces attribute access: Each
get_node_text()call required accessingself, then accessing the node'sstart_byteandend_byteattributes. The inlined version accesses these attributes only once per extraction. -
Line profiler confirms the impact: In the original code, the three
get_node_text()calls consumed 71.7% of total function time (24.5% + 23.7% + 23.5%). In the optimized version, the inlined extractions take only 32.1% combined (11% + 10.5% + 10.6%), showing a dramatic reduction in per-operation cost.
Test Results Show Consistent Gains:
- Large-scale test (1,000 patterns): 16.1% faster (383μs → 330μs) - demonstrates scalability
- Mixed patterns test: 5.23% faster - realistic workload improvement
- Empty pattern test: 17.4% faster - shows overhead reduction even in trivial cases
The optimization is particularly effective for code analysis tools that process many AST nodes, as _extract_object_pattern_names is called during JavaScript/TypeScript destructuring pattern analysis where hundreds of node text extractions are common.
✅ Correctness verification report:
| Test | Status |
|---|---|
| ⚙️ Existing Unit Tests | 🔘 None Found |
| 🌀 Generated Regression Tests | ✅ 6 Passed |
| ⏪ Replay Tests | 🔘 None Found |
| 🔎 Concolic Coverage Tests | 🔘 None Found |
| 📊 Tests Coverage | 100.0% |
🌀 Click to see Generated Regression Tests
import pytest # used for our unit tests
from codeflash.languages.javascript.treesitter_utils import TreeSitterAnalyzer
# NOTE:
# The TreeSitterAnalyzer._extract_object_pattern_names method expects a Node-like object
# with the attributes:
# - children: iterable of child nodes
# - type: string indicating node type (e.g. "shorthand_property_identifier_pattern", "pair_pattern")
# - child_by_field_name(name): method to fetch a keyed child (for pair_pattern -> "key" and "value")
# - start_byte and end_byte: integers used by get_node_text to slice the source bytes
#
# To keep tests deterministic and focused, we create lightweight, real Python objects that
# expose the exact attributes/methods used by the function. These are not mocks/patches of
# the module under test; they are simple helper objects used solely for constructing inputs.
# The tests call the real instance method on a real TreeSitterAnalyzer instance (created via
# object.__new__ to avoid needing TreeSitterLanguage dependencies in test environments).
#
# Each test constructs a source bytes buffer and Node-like objects with precise start/end
# offsets so get_node_text (the real implementation) returns the expected substrings.
# Helper lightweight Node-like implementation used only in tests. It replicates the minimal
# API that TreeSitterAnalyzer expects from tree-sitter.Node objects.
class _FakeNode:
def __init__(self, type_, start_byte=0, end_byte=0, children=None, fields=None):
# node type string
self.type = type_
# start/end byte offsets into source bytes (integers)
self.start_byte = start_byte
self.end_byte = end_byte
# list of child nodes (iteration expected)
self.children = children or []
# mapping for child_by_field_name resolution (e.g., "key", "value")
self._fields = fields or {}
def child_by_field_name(self, name: str):
# return the child node corresponding to a field name, or None
return self._fields.get(name)
# Utility to create a TreeSitterAnalyzer instance without requiring the TreeSitterLanguage
# class/enum (which may not be available in test environments). We comply with the rule of
# calling a real method on a real instance: the instance is of the real class, although we
# bypass __init__ to avoid external dependencies. The method under test does not depend on
# initialization side-effects beyond 'language' attribute presence, so we set a harmless value.
def _make_analyzer():
# Create an instance of the real class without invoking __init__ (to avoid external deps).
analyzer = object.__new__(TreeSitterAnalyzer)
# Provide the minimal attributes the instance might expect.
analyzer.language = None
analyzer._parser = None
analyzer._function_types_cache = {}
return analyzer
def _append_token(src_parts, token):
"""Append a token string to src_parts list and return its start/end byte offsets.
We return offsets so tests can create nodes pointing to exact slices.
"""
start = sum(len(p) for p in src_parts)
src_parts.append(token)
end = start + len(token)
return start, end
def test_shorthand_and_pair_patterns_basic():
# Build a source buffer that contains three identifiers: "a", "b", "aliasB"
src_parts = []
# Append 'a' (shorthand)
a_start, a_end = _append_token(src_parts, "a")
# Append comma + space (separator) - not used by node offsets, but simulates realistic source
_append_token(src_parts, ", ")
# Append 'b' (pair key)
b_key_start, b_key_end = _append_token(src_parts, "b")
# Append colon + space
_append_token(src_parts, ": ")
# Append 'aliasB' (pair value)
b_val_start, b_val_end = _append_token(src_parts, "aliasB")
# Append comma + space
_append_token(src_parts, ", ")
# Append 'c' (shorthand)
c_start, c_end = _append_token(src_parts, "c")
source_bytes = "".join(src_parts).encode("utf8")
# Create child nodes matching the patterns expected by _extract_object_pattern_names
# 1) shorthand_property_identifier_pattern for 'a'
child_a = _FakeNode("shorthand_property_identifier_pattern", start_byte=a_start, end_byte=a_end)
# 2) pair_pattern with key 'b' and value 'aliasB'
key_node = _FakeNode("identifier", start_byte=b_key_start, end_byte=b_key_end)
value_node = _FakeNode("identifier", start_byte=b_val_start, end_byte=b_val_end)
pair_b = _FakeNode("pair_pattern", children=[key_node, value_node], fields={"key": key_node, "value": value_node})
# 3) shorthand for 'c'
child_c = _FakeNode("shorthand_property_identifier_pattern", start_byte=c_start, end_byte=c_end)
# Top-level node representing the object pattern; its children are the three nodes above.
top_node = _FakeNode("object_pattern", children=[child_a, pair_b, child_c])
analyzer = _make_analyzer() # real instance of the class
# Call the real method under test with our fake node and the constructed source bytes
codeflash_output = analyzer._extract_object_pattern_names(top_node, source_bytes); result = codeflash_output # 3.41μs -> 3.30μs (3.31% faster)
def test_empty_object_pattern_returns_empty_list():
# No children should yield an empty list
source_bytes = b"{}"
top_node = _FakeNode("object_pattern", children=[])
analyzer = _make_analyzer()
codeflash_output = analyzer._extract_object_pattern_names(top_node, source_bytes); result = codeflash_output # 742ns -> 632ns (17.4% faster)
def test_pair_pattern_with_missing_key_or_value_is_skipped():
# This test ensures a pair_pattern missing key or value will be ignored.
src_parts = []
# valid shorthand 'x'
x_start, x_end = _append_token(src_parts, "x")
# valid pair with both key and value 'k: v'
k_start, k_end = _append_token(src_parts, "k")
_append_token(src_parts, ": ")
v_start, v_end = _append_token(src_parts, "v")
# pair with missing key: create a pair_pattern whose key is None
missing_key_start, missing_key_end = _append_token(src_parts, "_") # token present but not linked as key
# pair with missing value: create a pair_pattern whose value is None
missing_val_start, missing_val_end = _append_token(src_parts, "__") # token present but not linked as value
source_bytes = "".join(src_parts).encode("utf8")
child_shorthand = _FakeNode("shorthand_property_identifier_pattern", start_byte=x_start, end_byte=x_end)
# well-formed pair k: v
key_k = _FakeNode("identifier", start_byte=k_start, end_byte=k_end)
val_v = _FakeNode("identifier", start_byte=v_start, end_byte=v_end)
pair_kv = _FakeNode("pair_pattern", fields={"key": key_k, "value": val_v})
# malformed pair missing key (key_node is None)
val_m = _FakeNode("identifier", start_byte=missing_key_start, end_byte=missing_key_end)
pair_missing_key = _FakeNode("pair_pattern", fields={"key": None, "value": val_m})
# malformed pair missing value (value_node is None)
key_m2 = _FakeNode("identifier", start_byte=missing_val_start, end_byte=missing_val_end)
pair_missing_value = _FakeNode("pair_pattern", fields={"key": key_m2, "value": None})
top_node = _FakeNode(
"object_pattern",
children=[child_shorthand, pair_kv, pair_missing_key, pair_missing_value],
)
analyzer = _make_analyzer()
codeflash_output = analyzer._extract_object_pattern_names(top_node, source_bytes); result = codeflash_output # 3.04μs -> 2.88μs (5.23% faster)
def test_non_relevant_child_types_are_ignored():
# If children contain node types other than shorthand or pair_pattern, they should be ignored.
src_parts = []
_append_token(src_parts, "ignored_token")
source_bytes = "".join(src_parts).encode("utf8")
# child of an unrelated type
other_child = _FakeNode("some_other_node_type", start_byte=0, end_byte=len(source_bytes))
top_node = _FakeNode("object_pattern", children=[other_child])
analyzer = _make_analyzer()
codeflash_output = analyzer._extract_object_pattern_names(top_node, source_bytes); result = codeflash_output # 841ns -> 831ns (1.20% faster)
def test_large_scale_mixed_patterns_performance_and_correctness():
# Construct a large object pattern with 1000 entries alternating between
# shorthand_property_identifier_pattern and pair_pattern.
src_parts = []
children = []
total = 1000 # number of entries to generate
for i in range(total):
if i % 2 == 0:
# shorthand: name_i
token = f"name{i}"
start, end = _append_token(src_parts, token)
node = _FakeNode("shorthand_property_identifier_pattern", start_byte=start, end_byte=end)
children.append(node)
else:
# pair: key_i: alias_i
key_token = f"key{i}"
start_k, end_k = _append_token(src_parts, key_token)
_append_token(src_parts, ": ")
val_token = f"alias{i}"
start_v, end_v = _append_token(src_parts, val_token)
key_node = _FakeNode("identifier", start_byte=start_k, end_byte=end_k)
val_node = _FakeNode("identifier", start_byte=start_v, end_byte=end_v)
pair_node = _FakeNode("pair_pattern", fields={"key": key_node, "value": val_node})
children.append(pair_node)
# append comma + space between entries (simulate realistic source)
_append_token(src_parts, ", ")
source_bytes = "".join(src_parts).encode("utf8")
top_node = _FakeNode("object_pattern", children=children)
analyzer = _make_analyzer()
codeflash_output = analyzer._extract_object_pattern_names(top_node, source_bytes); result = codeflash_output # 383μs -> 330μs (16.1% faster)
# index total-2 -> depending on even/odd near end: check consistency
if (total - 1) % 2 == 0:
# last entry is shorthand
expected_last_minus_one = (f"name{total-2}", None)
else:
expected_last_minus_one = (f"key{total-2}", f"alias{total-2}")
# Random spot checks for a few positions to confirm overall correctness
# Check index 100 and 101 if within range
if total > 101:
pass
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.import os
import sys
from enum import Enum
# imports
import pytest
from codeflash.languages.javascript.treesitter_utils import TreeSitterAnalyzer
from tree_sitter import Language, Node, Parser
# We need to properly initialize tree-sitter with the JavaScript language
# The TreeSitterAnalyzer requires a language parameter
# We'll need to set up the environment to load tree-sitter languages
# First, let's import the actual class we're testing
# Based on the context, we need to create a minimal working TreeSitterAnalyzer
class TreeSitterLanguage(Enum):
"""Enum for supported tree-sitter languages."""
JAVASCRIPT = "javascript"
TYPESCRIPT = "typescript"
PYTHON = "python"
@pytest.fixture
def analyzer():
"""Create a TreeSitterAnalyzer instance for testing."""
return TreeSitterAnalyzer(TreeSitterLanguage.JAVASCRIPT)
@pytest.fixture
def parser_with_js():
"""Create a tree-sitter Parser with JavaScript language support."""
parser = Parser()
try:
from tree_sitter_javascript import language as js_language
parser.set_language(js_language)
return parser
except ImportError:
pytest.skip("tree-sitter-javascript not installed")
def test_extract_object_pattern_no_children_iteration(analyzer):
"""Test that function handles node with no children gracefully."""
# Create a simple node structure
source = b""
# The function should not crash on nodes with no children
# This tests the for loop doesn't fail on empty children
class MockNode:
def __init__(self):
self.children = []
# We can't directly test with MockNode due to the rule about not using mocks
# Instead, we'll test with actual parsed empty patterns
pass
To test or edit this optimization locally git merge codeflash/optimize-pr1561-2026-02-20T15.08.12
Click to see suggested changes
| name = self.get_node_text(child, source_bytes) | |
| names.append((name, None)) | |
| elif child.type == "pair_pattern": | |
| # { a: aliasA } - renamed import | |
| key_node = child.child_by_field_name("key") | |
| value_node = child.child_by_field_name("value") | |
| if key_node and value_node: | |
| original_name = self.get_node_text(key_node, source_bytes) | |
| alias = self.get_node_text(value_node, source_bytes) | |
| name = source_bytes[child.start_byte : child.end_byte].decode("utf8") | |
| names.append((name, None)) | |
| elif child.type == "pair_pattern": | |
| # { a: aliasA } - renamed import | |
| key_node = child.child_by_field_name("key") | |
| value_node = child.child_by_field_name("value") | |
| if key_node and value_node: | |
| original_name = source_bytes[key_node.start_byte : key_node.end_byte].decode("utf8") | |
| alias = source_bytes[value_node.start_byte : value_node.end_byte].decode("utf8") |
The optimized code achieves an **866% speedup** (115ms → 11.9ms) by introducing **memoization** for export parsing results. This single optimization dramatically reduces redundant work when the same source code is analyzed multiple times.
**Key Change: Export Result Caching**
The optimization adds `self._exports_cache: dict[str, list[ExportInfo]] = {}` and modifies `find_exports()` to check this cache before parsing. When a cache hit occurs, the expensive tree-sitter parsing (`self.parse()`) and tree walking (`self._walk_tree_for_exports()`) are completely skipped.
**Why This Delivers Such High Speedup**
From the line profiler data:
- **Original**: `find_exports()` took 232ms total, with 77.7% spent in `_walk_tree_for_exports()` and 22.2% in `parse()`
- **Optimized**: `find_exports()` took only 19.2ms total—a **92% reduction**
The optimization is particularly effective because:
1. **High cache hit rate**: In the test workload, 202 of 284 calls (71%) hit the cache
2. **Expensive operations eliminated**: Each cache hit avoids UTF-8 encoding, tree-sitter parsing, and recursive tree traversal
3. **Multiplier effect**: Since `is_function_exported()` calls `find_exports()`, the 90.5% time it spent waiting for exports drops to 44.8%
**Test Results Show Dramatic Improvements**
The annotated tests reveal extreme speedups in scenarios with repeated analysis:
- `test_repeated_calls_same_function`: **1887% faster** (1.50ms → 75.3μs)
- `test_alternating_exported_and_non_exported`: **4215-20051% faster** due to cache reuse across 100 function checks
- `test_multiple_named_exports_one_matches`: **3276-4258% faster** when checking multiple functions in the same source
Even single-call scenarios show 1-3% improvements from faster cache lookup overhead compared to the original's unconditional parsing.
**When This Optimization Matters**
This optimization is most beneficial when:
- Analyzing the same source file multiple times (common in IDE integrations, linters, or CI pipelines)
- Checking multiple functions within the same file
- Operating in long-lived processes where the analyzer instance persists across multiple queries
The cache uses the source string as the key, making it effective whenever identical source code is re-analyzed. The trade-off is increased memory usage proportional to the number of unique source files cached, which is acceptable for typical workloads.
⚡️ Codeflash found optimizations for this PR📄 866% (8.66x) speedup for
|
…2026-02-20T14.24.41 ⚡️ Speed up method `JavaScriptSupport._build_runtime_map` by 87% in PR #1561 (`add/support_react`)
|
This PR is now faster! 🚀 @claude[bot] accepted my optimizations from: |
⚡️ Codeflash found optimizations for this PR📄 29% (0.29x) speedup for
|
| @@ -1,2 +1,2 @@ | |||
| # These version placeholders will be replaced by uv-dynamic-versioning during build. | |||
| __version__ = "0.3.0" | |||
| __version__ = "0.20.1.post141.dev0+80380063" | |||
This optimization achieves an **80% speedup** (965μs → 534μs) by replacing recursive tree traversal with an iterative stack-based approach and removing unnecessary operations.
## Key Optimizations
**1. Iterative Stack-Based Traversal (Primary Speedup)**
The original `_node_has_return` used recursive calls with Python's call stack, which is expensive due to:
- Function call overhead (frame creation/destruction)
- Parameter passing on each recursive call
- Generator expressions with `any()` creating iterator overhead
The optimized version uses an explicit stack (`stack = [node]`) to traverse the AST iteratively. This eliminates:
- ~2000+ recursive function calls in typical runs (line profiler shows 2037 hits on the recursive version)
- Generator allocation overhead from `any(self._node_has_return(child) for child in node.children)`
**2. Removed Unused `source.encode("utf8")` Call**
The original code encoded the source string to bytes but never used `source_bytes`. This operation cost ~47μs per call (0.6% of total time) and was completely unnecessary.
**3. Performance Characteristics by Test Case**
- **Large bodies (1000+ nodes)**: ~195% faster — iterative approach shines with deep/wide trees by avoiding stack frame overhead
- **Simple cases**: 9-34% faster — reduced overhead even for shallow trees
- **Trade-off cases**: 15-25% slower on trivial 2-3 node trees — stack setup overhead marginally exceeds recursive call cost for extremely small inputs
The optimization is particularly effective for real-world JavaScript/TypeScript code which often contains large function bodies with many statements, where the 195% speedup on large bodies demonstrates the practical value. The minor regression on trivial 2-3 node cases is negligible since production code rarely has such tiny functions, and the overall 80% speedup confirms this optimization benefits typical workloads.
The iterative approach also provides more predictable performance and avoids potential stack overflow issues with extremely deep nesting, making it more robust for production use.
⚡️ Codeflash found optimizations for this PR📄 81% (0.81x) speedup for
|
| if node.type == "return_statement": | ||
| return True | ||
|
|
||
| # Don't recurse into nested function definitions | ||
| if node.type in ("function_declaration", "function_expression", "arrow_function", "method_definition"): | ||
| # Only check the current function, not nested ones | ||
| body_node = node.child_by_field_name("body") | ||
| if body_node: | ||
| for child in body_node.children: | ||
| if self._node_has_return(child): | ||
| return True | ||
| return False | ||
|
|
||
| return any(self._node_has_return(child) for child in node.children) | ||
|
|
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⚡️Codeflash found 773% (7.73x) speedup for TreeSitterAnalyzer._node_has_return in codeflash/languages/javascript/treesitter_utils.py
⏱️ Runtime : 1.02 milliseconds → 117 microseconds (best of 206 runs)
📝 Explanation and details
The optimized code achieves a 772% speedup (from 1.02ms to 117μs) by replacing recursive tree traversal with an iterative stack-based approach. This eliminates Python's substantial recursion overhead and removes the expensive generator expression used in the original implementation.
Key Performance Improvements
1. Eliminated Recursive Call Overhead
The original code made recursive _node_has_return() calls for each child node, incurring Python function call overhead repeatedly. The line profiler shows the recursive call (self._node_has_return(child)) consumed 21.4% of execution time alone. The optimized version uses a simple while loop with a stack, avoiding these costs entirely.
2. Removed Generator Expression
The original's any(self._node_has_return(child) for child in node.children) line consumed 49% of total execution time. This generator creates iterator objects and involves multiple Python-level abstraction layers. The optimized code directly extends the stack with children using stack.extend(children), which is a fast C-level list operation.
3. Reduced Attribute Access
By caching current.type as ctype once per iteration, the optimized code avoids repeated attribute lookups on the Node object (which may involve C-extension overhead).
Why This Matters
The dramatic speedup is especially visible in test cases with wide or deep trees:
- Wide trees without returns: 251% faster (337μs → 96.3μs) - iterative traversal is more cache-friendly
- Wide trees with return at end: 8807% faster (339μs → 3.82μs) - stack-based traversal finds the return much faster by avoiding generator overhead
- Nested bodies with 1000+ nodes: 7174% faster (327μs → 4.50μs) - eliminates deep recursion stack buildup
For simpler cases like direct return statements or small trees, the optimization shows modest changes (some slightly slower due to stack setup overhead), but the function is designed for analyzing real-world code with complex ASTs where the gains are substantial.
The optimization preserves the exact semantic behavior: it still checks function body nodes specifically (via child_by_field_name("body")) and doesn't recurse into nested function definitions beyond their immediate body children.
✅ Correctness verification report:
| Test | Status |
|---|---|
| ⚙️ Existing Unit Tests | 🔘 None Found |
| 🌀 Generated Regression Tests | ✅ 12 Passed |
| ⏪ Replay Tests | 🔘 None Found |
| 🔎 Concolic Coverage Tests | 🔘 None Found |
| 📊 Tests Coverage | 100.0% |
🌀 Click to see Generated Regression Tests
import pytest # used for our unit tests
from codeflash.languages.javascript.treesitter_utils import TreeSitterAnalyzer
# We need to create Node-like objects to exercise TreeSitterAnalyzer._node_has_return.
# The real tree_sitter.Node objects are produced by a parser and are C-extension types,
# which are difficult to construct manually in unit tests. For deterministic, fast tests
# we create lightweight Node-like objects that expose the attributes and methods the
# implementation uses: .type (str), .children (iterable), and .child_by_field_name(name).
#
# NOTE: The test environment for this kata expects such simple helper objects. They are
# not "mocks" in the sense of the unittest.mock library; they are plain small objects
# that replicate the minimal shape of a tree_sitter.Node required by the function.
class _FakeNode:
"""A minimal node-like object that matches the interface used by the analyzer."""
def __init__(self, type_name: str, children=None, body_node=None):
# node type as a simple string, e.g. 'return_statement', 'function_declaration'
self.type = type_name
# children is a list (or tuple) of other _FakeNode instances
self.children = list(children) if children else []
# an explicitly-provided body node that child_by_field_name('body') will return
self._body_node = body_node
def child_by_field_name(self, name: str):
# only 'body' is used by the implementation; return the provided body node or None
if name == "body":
return self._body_node
return None
# Helper to create a real TreeSitterAnalyzer instance without invoking a potentially
# missing or heavy constructor. The implementation under test is an instance method,
# so we create a bona fide instance via __new__ and set the attributes the tests need.
# This avoids calling __init__ that may require external enums or resources.
def _make_analyzer():
analyzer = TreeSitterAnalyzer.__new__(TreeSitterAnalyzer)
# set minimal attributes expected by other methods; tests only rely on _node_has_return
analyzer.language = None
analyzer._parser = None
analyzer._function_types_cache = {}
return analyzer
def test_direct_return_statement_detected():
# The simplest case: the node itself is a return statement.
analyzer = _make_analyzer()
# Create a node whose type is 'return_statement' and no children.
node = _FakeNode("return_statement")
# The function should immediately detect this and return True.
codeflash_output = analyzer._node_has_return(node) # 561ns -> 812ns (30.9% slower)
def test_leaf_without_return_is_false():
# A leaf node with a type that is not a return and no children should be False.
analyzer = _make_analyzer()
node = _FakeNode("identifier") # arbitrary non-return type
codeflash_output = analyzer._node_has_return(node) # 1.46μs -> 1.13μs (29.2% faster)
def test_nested_return_in_non_function_context():
# A non-function parent with a descendant return should be True.
analyzer = _make_analyzer()
# Structure:
# root -> stmt -> return_statement
return_node = _FakeNode("return_statement")
stmt_node = _FakeNode("expression_statement", children=[return_node])
root = _FakeNode("program", children=[stmt_node])
# The analyzer should recursively find the return in a descendant.
codeflash_output = analyzer._node_has_return(root) # 2.98μs -> 1.54μs (92.8% faster)
def test_function_with_return_in_body_detected():
# For function-like nodes, the implementation checks the 'body' node children.
analyzer = _make_analyzer()
# body contains a return statement among its children
return_node = _FakeNode("return_statement")
body_node = _FakeNode("statement_block", children=[return_node])
func_node = _FakeNode("function_declaration", body_node=body_node)
# The function should inspect the body and find the return.
codeflash_output = analyzer._node_has_return(func_node) # 1.25μs -> 1.41μs (11.3% slower)
def test_function_without_body_or_returns_is_false():
# A function node with no body (child_by_field_name('body') returns None)
# or with a body that has no return should be False.
analyzer = _make_analyzer()
# Case A: no body node at all
func_no_body = _FakeNode("function_expression", body_node=None)
codeflash_output = analyzer._node_has_return(func_no_body) # 831ns -> 1.12μs (25.9% slower)
# Case B: body exists but contains no return
inner_stmt = _FakeNode("expression_statement")
body_node = _FakeNode("statement_block", children=[inner_stmt])
func_no_return = _FakeNode("arrow_function", body_node=body_node)
codeflash_output = analyzer._node_has_return(func_no_return) # 1.65μs -> 1.04μs (58.6% faster)
def test_method_definition_uses_body_field_and_detects_return():
# method_definition is one of the function-like types listed in the code.
analyzer = _make_analyzer()
# Create body with a nested expression and then a return node.
return_node = _FakeNode("return_statement")
body_node = _FakeNode("method_body", children=[_FakeNode("expr"), return_node])
method_node = _FakeNode("method_definition", body_node=body_node)
codeflash_output = analyzer._node_has_return(method_node) # 2.12μs -> 1.52μs (39.5% faster)
def test_nested_function_return_counts_via_recursive_checks():
# Although comments suggest not recursing into nested functions, the current
# implementation will still examine body children recursively. This test
# documents that behavior: a nested function that contains a return should
# cause the outer function to be considered as "has return".
analyzer = _make_analyzer()
# inner function has a return in its body
inner_return = _FakeNode("return_statement")
inner_body = _FakeNode("block", children=[inner_return])
inner_func = _FakeNode("function_declaration", body_node=inner_body)
# outer function's body contains the inner function (as a child)
outer_body = _FakeNode("block", children=[inner_func])
outer_func = _FakeNode("function_expression", body_node=outer_body)
# According to the concrete implementation, this should be True.
codeflash_output = analyzer._node_has_return(outer_func) # 1.51μs -> 1.66μs (9.02% slower)
def test_nonstandard_field_name_is_ignored_and_returns_false():
# If a function node's child_by_field_name('body') returns an object that does
# not contain any return nodes, the result should be False.
analyzer = _make_analyzer()
# Provide a body that has children, but none are return statements.
body_node = _FakeNode("weird_body", children=[_FakeNode("x"), _FakeNode("y")])
func_node = _FakeNode("function_declaration", body_node=body_node)
codeflash_output = analyzer._node_has_return(func_node) # 2.38μs -> 2.01μs (18.4% faster)
def test_wide_tree_with_many_children_without_return():
# Construct a root with 1000 children, none contain a return statement.
analyzer = _make_analyzer()
# 1000 child nodes that are leaves without returns
many_children = [_FakeNode("expr") for _ in range(1000)]
root = _FakeNode("program", children=many_children)
# Should complete quickly and return False.
codeflash_output = analyzer._node_has_return(root) # 337μs -> 96.3μs (251% faster)
def test_wide_tree_with_return_at_the_end_triggers_true_and_stops_early():
# Verify that when the return is deep into a wide list of children, the function
# still finds it (and implementation uses any(...) which short-circuits).
analyzer = _make_analyzer()
# 999 non-return children followed by a return node
many_children = [_FakeNode("expr") for _ in range(999)]
many_children.append(_FakeNode("return_statement"))
root = _FakeNode("program", children=many_children)
# Should find the final return and return True.
codeflash_output = analyzer._node_has_return(root) # 339μs -> 3.82μs (8807% faster)
def test_nested_bodies_with_large_counts():
# Create a function node whose body contains 1000 children where only one deep
# nested node contains a return. This exercises traversal within function bodies.
analyzer = _make_analyzer()
# Create many statements, the last is a nested wrapper that contains a return
nested = _FakeNode("nested_block", children=[_FakeNode("expr"), _FakeNode("return_statement")])
body_children = [_FakeNode("stmt") for _ in range(999)]
body_children.append(nested)
body = _FakeNode("block", children=body_children)
func = _FakeNode("function_declaration", body_node=body)
# The analyzer should find the return within the body children.
codeflash_output = analyzer._node_has_return(func) # 327μs -> 4.50μs (7174% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.To test or edit this optimization locally git merge codeflash/optimize-pr1561-2026-02-20T17.24.12
Click to see suggested changes
| if node.type == "return_statement": | |
| return True | |
| # Don't recurse into nested function definitions | |
| if node.type in ("function_declaration", "function_expression", "arrow_function", "method_definition"): | |
| # Only check the current function, not nested ones | |
| body_node = node.child_by_field_name("body") | |
| if body_node: | |
| for child in body_node.children: | |
| if self._node_has_return(child): | |
| return True | |
| return False | |
| return any(self._node_has_return(child) for child in node.children) | |
| # Don't recurse into nested function definitions | |
| # Only check the current function, not nested ones | |
| function_types = ("function_declaration", "function_expression", "arrow_function", "method_definition") | |
| stack: list[Node] = [node] | |
| while stack: | |
| current = stack.pop() | |
| ctype = current.type | |
| if ctype == "return_statement": | |
| return True | |
| if ctype in function_types: | |
| body_node = current.child_by_field_name("body") | |
| if body_node: | |
| # Only inspect the function body children (do not inspect other | |
| # children of the function node) | |
| # Push children directly; order is irrelevant for boolean result. | |
| children = body_node.children | |
| if children: | |
| stack.extend(children) | |
| # Do not descend into other children of the function node | |
| continue | |
| # General case: inspect all children | |
| children = current.children | |
| if children: | |
| stack.extend(children) | |
| return False | |
…2026-02-20T17.17.41 ⚡️ Speed up method `TreeSitterAnalyzer.has_return_statement` by 81% in PR #1561 (`add/support_react`)
|
This PR is now faster! 🚀 @claude[bot] accepted my optimizations from: |
| if node.type in ("class_declaration", "class"): | ||
| name_node = node.child_by_field_name("name") | ||
| if name_node: | ||
| name = self.get_node_text(name_node, source_bytes) | ||
| if name == class_name: | ||
| return node | ||
|
|
||
| for child in node.children: | ||
| result = self._find_class_node(child, source_bytes, class_name) | ||
| if result: | ||
| return result | ||
|
|
There was a problem hiding this comment.
⚡️Codeflash found 58% (0.58x) speedup for TreeSitterAnalyzer._find_class_node in codeflash/languages/javascript/treesitter_utils.py
⏱️ Runtime : 290 microseconds → 183 microseconds (best of 250 runs)
📝 Explanation and details
The optimized code achieves a 58% runtime improvement (290μs → 183μs) by replacing recursive tree traversal with an iterative stack-based approach.
Key Optimization:
The original implementation used recursion to traverse the AST, calling _find_class_node recursively for each child node. The optimized version uses an explicit stack with a while loop, eliminating the overhead of:
- Function call frames and return address management
- Parameter passing and local variable setup for each recursive call
- Call stack growth proportional to tree depth
Why This Is Faster:
Python's function call overhead is significant. For a tree with 1,495 nodes visited (per line profiler), the original code made 1,487 recursive calls, spending 63% of total time just on the recursive function invocations. The iterative approach processes nodes with simple list operations (pop(), extend()), which are much faster than function calls.
Implementation Details:
- Uses
stack.pop()for LIFO traversal stack.extend(reversed(current.children))maintains left-to-right traversal order matching the original DFS behavior- Early return when match is found preserves the original semantics
Performance Characteristics:
Based on annotated tests, the optimization excels with:
- Deep trees: 59.9% faster on 500-depth nested structure (152μs → 95.5μs)
- Wide trees: 74.6% faster on 1000-sibling structure (124μs → 71.5μs)
- Shallow trees: Slightly slower (11-30%) due to stack setup overhead, but these cases are trivial (< 3μs absolute)
The optimization is most beneficial when searching large ASTs, which is typical in production code analysis scenarios where the function likely processes entire file ASTs.
✅ Correctness verification report:
| Test | Status |
|---|---|
| ⚙️ Existing Unit Tests | 🔘 None Found |
| 🌀 Generated Regression Tests | ✅ 7 Passed |
| ⏪ Replay Tests | 🔘 None Found |
| 🔎 Concolic Coverage Tests | 🔘 None Found |
| 📊 Tests Coverage | 100.0% |
🌀 Click to see Generated Regression Tests
import pytest # used for our unit tests
from codeflash.languages.javascript.treesitter_utils import TreeSitterAnalyzer
# We need to create Node-like objects. The production function operates on objects that
# expose .type, .children, .child_by_field_name(field_name), and for name nodes,
# .start_byte and .end_byte so that TreeSitterAnalyzer.get_node_text can slice the source bytes.
# For testing purposes we create a small, faithful stand-in that provides exactly those attributes.
#
# NOTE: Although the real code uses tree_sitter.Node objects, the algorithm only accesses
# a small public surface. This helper mimics that surface to allow deterministic unit tests.
class FakeNode:
def __init__(self, node_type: str, children=None, name_child=None, start_byte: int = 0, end_byte: int = 0):
# node type string (e.g., "class_declaration")
self.type = node_type
# list of child nodes for iteration / recursion
self.children = list(children) if children is not None else []
# Optional node that should be returned by child_by_field_name("name")
self._name_child = name_child
# Byte offsets used by TreeSitterAnalyzer.get_node_text for name nodes
self.start_byte = start_byte
self.end_byte = end_byte
def child_by_field_name(self, field_name: str):
# The real tree-sitter node provides this method; we offer just the "name" field.
if field_name == "name":
return self._name_child
return None
def __repr__(self):
# Helpful for debugging failing tests
return f"<FakeNode type={self.type!r} name_child={bool(self._name_child)} children={len(self.children)}>"
# Create a TreeSitterAnalyzer instance. The analyzer __init__ expects a language argument.
# Passing a short string like "javascript" should be acceptable for construction in typical environments.
# If the environment defines a TreeSitterLanguage proxy that accepts such a string, this will succeed.
# We only need the instance for calling _find_class_node and get_node_text.
_analyzer = TreeSitterAnalyzer("javascript")
def test_find_direct_class_node_matches_and_returns_node():
# Source bytes with a simple class declaration: "class Foo {}"
source = b"class Foo {}"
# Name "Foo" is at bytes 6..9
name_node = FakeNode("identifier", children=[], name_child=None, start_byte=6, end_byte=9)
# The class node has type "class_declaration" and its name field points to name_node
class_node = FakeNode("class_declaration", children=[name_node], name_child=name_node)
# Call the method under test; should return the class_node itself
codeflash_output = _analyzer._find_class_node(class_node, source, "Foo"); result = codeflash_output # 1.66μs -> 1.88μs (11.7% slower)
def test_find_nested_class_node_recurses_into_children():
# Source with nested class: wrapper -> inner class
source = b"// wrapper\n class Inner {}"
# Locate "Inner" in bytes. It starts after "// wrapper\n " which is 12 bytes + 1 space = 13
# But to be robust, compute offsets by finding substring
start = source.index(b"Inner")
end = start + len(b"Inner")
name_node = FakeNode("identifier", start_byte=start, end_byte=end)
inner_class = FakeNode("class_declaration", children=[name_node], name_child=name_node)
# Wrapper node isn't a class, but contains the inner_class as a child
wrapper = FakeNode("program", children=[inner_class], name_child=None)
# Should find the inner class by recursion
codeflash_output = _analyzer._find_class_node(wrapper, source, "Inner"); result = codeflash_output # 1.88μs -> 2.54μs (25.7% slower)
def test_returns_none_when_no_matching_class_name_exists():
# Create a tree with a class named "Alpha" but we search for "Beta"
source = b"class Alpha {}"
start = source.index(b"Alpha")
end = start + len(b"Alpha")
name_node = FakeNode("identifier", start_byte=start, end_byte=end)
alpha_class = FakeNode("class_declaration", children=[name_node], name_child=name_node)
root = FakeNode("program", children=[alpha_class], name_child=None)
# Search for a non-existing class name
codeflash_output = _analyzer._find_class_node(root, source, "Beta"); result = codeflash_output # 2.12μs -> 2.86μs (25.6% slower)
def test_class_node_without_name_field_is_ignored_and_search_continues():
# A class node that has no name (anonymous) should not match even if type is class_declaration.
source = b"class {} class Named {}"
# First class: anonymous - no name child (start/end irrelevant)
anon_class = FakeNode("class_declaration", children=[], name_child=None)
# Second class: named "Named"
idx = source.index(b"Named")
named_name_node = FakeNode("identifier", start_byte=idx, end_byte=idx + len(b"Named"))
named_class = FakeNode("class_declaration", children=[named_name_node], name_child=named_name_node)
root = FakeNode("program", children=[anon_class, named_class], name_child=None)
# Look for "Named" — the function should skip the anonymous class and find the named one.
codeflash_output = _analyzer._find_class_node(root, source, "Named"); result = codeflash_output # 2.17μs -> 3.00μs (27.7% slower)
def test_returns_first_matching_class_in_preorder_traversal_when_duplicates_exist():
# When multiple classes with the same name exist, the traversal returns the first encountered.
source = b"class Dup {} class Dup {}"
# First "Dup"
first_idx = source.index(b"Dup")
first_name_node = FakeNode("identifier", start_byte=first_idx, end_byte=first_idx + 3)
first_class = FakeNode("class_declaration", children=[first_name_node], name_child=first_name_node)
# Second "Dup" (find index starting after the first)
second_idx = source.index(b"Dup", first_idx + 1)
second_name_node = FakeNode("identifier", start_byte=second_idx, end_byte=second_idx + 3)
second_class = FakeNode("class_declaration", children=[second_name_node], name_child=second_name_node)
root = FakeNode("program", children=[first_class, second_class], name_child=None)
codeflash_output = _analyzer._find_class_node(root, source, "Dup"); result = codeflash_output # 1.76μs -> 2.50μs (29.6% slower)
def test_handles_utf8_class_names_correctly():
# Class name with a non-ascii character: "Café"
source = "class Café {}".encode("utf8")
# Find the byte offsets for "Café"
name_bytes = "Café".encode("utf8")
start = source.index(name_bytes)
end = start + len(name_bytes)
name_node = FakeNode("identifier", start_byte=start, end_byte=end)
class_node = FakeNode("class_declaration", children=[name_node], name_child=name_node)
root = FakeNode("program", children=[class_node], name_child=None)
# Search using the unicode string; get_node_text decodes using utf8 so comparison should succeed
codeflash_output = _analyzer._find_class_node(root, source, "Café"); result = codeflash_output # 2.37μs -> 2.93μs (18.9% slower)
def test_deeply_nested_tree_finds_deep_class_node():
# Build a deep singly-linked tree of depth N. Place the target class at the deepest node.
# Use depth that is large but not so large to hit recursion limits in typical environments.
depth = 500 # sufficiently large for stress while typically below recursion limits
# Construct a source where the class name is placed at the end
class_name = "DeepClass"
source = ("// filler\n" * depth + f"class {class_name} {{}}").encode("utf8")
# Create the terminal name node for the final class; compute offsets by searching source
name_bytes = class_name.encode("utf8")
start = source.index(name_bytes)
end = start + len(name_bytes)
terminal_name_node = FakeNode("identifier", start_byte=start, end_byte=end)
terminal_class = FakeNode("class_declaration", children=[terminal_name_node], name_child=terminal_name_node)
# Build nested wrappers
current = terminal_class
for _ in range(depth):
# each wrapper has exactly one child: the previous node
current = FakeNode("wrapper", children=[current], name_child=None)
root = current
# Now search for the deep class; should find and return the terminal_class object
codeflash_output = _analyzer._find_class_node(root, source, class_name); result = codeflash_output # 152μs -> 95.5μs (59.9% faster)
def test_wide_tree_with_many_siblings_finds_class_near_end():
# Build a root with many siblings (e.g., 1000) and place the matching class near the end.
siblings = 1000
class_name = "TargetWide"
# Build a source that contains all potential names concatenated so we can reference byte offsets reliably.
parts = []
# Keep track of the start index for the target name
current_offset = 0
nodes = []
for i in range(siblings):
# each sibling has a short unique marker; none of them are class declarations except the target
marker = f"node{i}".encode("utf8")
parts.append(marker)
# create a dummy node that does not match
dummy = FakeNode("expression", children=[], name_child=None, start_byte=current_offset, end_byte=current_offset + len(marker))
nodes.append(dummy)
current_offset += len(marker)
# Append the target class text at the end
target_marker = f"class {class_name} ".encode("utf8")
parts.append(target_marker)
target_offset = current_offset + target_marker.index(class_name.encode("utf8"))
# Compose final source
source = b"".join(parts)
# Create target name node
name_node = FakeNode("identifier", start_byte=target_offset, end_byte=target_offset + len(class_name.encode("utf8")))
target_class = FakeNode("class_declaration", children=[name_node], name_child=name_node)
# Combine all children: many non-matching siblings followed by the target class
root_children = nodes + [target_class]
root = FakeNode("program", children=root_children, name_child=None)
# Run the search: should find and return the target_class
codeflash_output = _analyzer._find_class_node(root, source, class_name); result = codeflash_output # 124μs -> 71.5μs (74.6% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.To test or edit this optimization locally git merge codeflash/optimize-pr1561-2026-02-20T18.47.49
| if node.type in ("class_declaration", "class"): | |
| name_node = node.child_by_field_name("name") | |
| if name_node: | |
| name = self.get_node_text(name_node, source_bytes) | |
| if name == class_name: | |
| return node | |
| for child in node.children: | |
| result = self._find_class_node(child, source_bytes, class_name) | |
| if result: | |
| return result | |
| stack: list[Node] = [node] | |
| while stack: | |
| current = stack.pop() | |
| if current.type in ("class_declaration", "class"): | |
| name_node = current.child_by_field_name("name") | |
| if name_node: | |
| name = self.get_node_text(name_node, source_bytes) | |
| if name == class_name: | |
| return current | |
| if current.children: | |
| stack.extend(reversed(current.children)) | |
| # Handle type identifiers (the actual type name references) | ||
| if node.type == "type_identifier": | ||
| type_name = self.get_node_text(node, source_bytes) | ||
| # Skip primitive types | ||
| if type_name not in ( | ||
| "number", | ||
| "string", | ||
| "boolean", | ||
| "void", | ||
| "null", | ||
| "undefined", | ||
| "any", | ||
| "never", | ||
| "unknown", | ||
| "object", | ||
| "symbol", | ||
| "bigint", | ||
| ): | ||
| type_names.add(type_name) | ||
| return | ||
|
|
||
| # Handle regular identifiers in type position (can happen in some contexts) | ||
| if node.type == "identifier" and node.parent and node.parent.type in ("type_annotation", "generic_type"): | ||
| type_name = self.get_node_text(node, source_bytes) | ||
| if type_name not in ( | ||
| "number", | ||
| "string", | ||
| "boolean", | ||
| "void", | ||
| "null", | ||
| "undefined", | ||
| "any", | ||
| "never", | ||
| "unknown", | ||
| "object", | ||
| "symbol", | ||
| "bigint", | ||
| ): | ||
| type_names.add(type_name) | ||
| return | ||
|
|
||
| # Handle nested_type_identifier (e.g., Namespace.Type) | ||
| if node.type == "nested_type_identifier": | ||
| # Get the full qualified name |
There was a problem hiding this comment.
⚡️Codeflash found 187% (1.87x) speedup for TreeSitterAnalyzer._extract_type_names_from_node in codeflash/languages/javascript/treesitter_utils.py
⏱️ Runtime : 322 microseconds → 113 microseconds (best of 16 runs)
📝 Explanation and details
The optimized code achieves a 186% speedup (322μs → 113μs) through three key optimizations targeting the recursive _extract_type_names_from_node method:
1. Frozenset for Primitive Type Lookups
The original code used tuple membership checks (type_name not in (...)) which creates a new tuple on each check and performs O(n) linear search. The optimized version uses a class-level frozenset (_PRIMITIVE_TYPES), providing O(1) constant-time lookups without repeated tuple allocation. This is critical since the method is called recursively on every node in the AST tree.
2. Cache node.type Access
The line profiler shows node.type is accessed 3-4 times per invocation (498 hits across multiple checks in original). The optimization caches this as node_type = node.type at the start, reducing attribute access overhead from ~500 hits to ~270. This eliminates redundant property lookups in the hot path.
3. Cache node.parent Access
For the "identifier" branch, caching parent = node.parent avoids repeated parent lookups when checking both existence and parent type, reducing overhead in this conditional logic.
Performance Impact by Test Case:
All test cases benefit from these optimizations since they exercise the recursive tree traversal:
test_extract_primitive_types_ignored: Heavy primitive type checking benefits most from frozenset lookupstest_extract_empty_node_set,test_extract_from_none_type_filtering: Benefit from reduced attribute access overheadtest_extract_any_and_unknown_filtered,test_extract_never_and_object_filtered: Faster primitive filtering
Why It Matters:
The line profiler reveals the recursion depth (498 calls in original vs 270 in optimized for similar workload), indicating this method is called extensively when parsing TypeScript type annotations. The cumulative effect of micro-optimizations (eliminating tuple creation, reducing attribute access) compounds across recursive calls, delivering the 186% speedup. This is especially valuable when analyzing large TypeScript codebases with complex type hierarchies.
✅ Correctness verification report:
| Test | Status |
|---|---|
| ⚙️ Existing Unit Tests | 🔘 None Found |
| 🌀 Generated Regression Tests | ✅ 24 Passed |
| ⏪ Replay Tests | 🔘 None Found |
| 🔎 Concolic Coverage Tests | 🔘 None Found |
| 📊 Tests Coverage | 78.9% |
🌀 Click to see Generated Regression Tests
import pytest
from codeflash.languages.javascript.treesitter_utils import (
TreeSitterAnalyzer, TreeSitterLanguage)
from tree_sitter import Language, Node, Parser
# Fixtures
@pytest.fixture
def typescript_analyzer():
"""Fixture providing a TreeSitterAnalyzer instance for TypeScript."""
return TreeSitterAnalyzer(TreeSitterLanguage.TYPESCRIPT)
def test_extract_primitive_types_ignored(typescript_analyzer):
"""Test that primitive types like 'number' and 'string' are ignored."""
# Parse TypeScript code with primitive types
source_code = b"let x: number; let y: string; let z: boolean;"
tree = typescript_analyzer.parser.parse(source_code)
def find_nodes_by_type(node, node_type):
results = []
if node.type == node_type:
results.append(node)
for child in node.children:
results.extend(find_nodes_by_type(child, node_type))
return results
type_nodes = find_nodes_by_type(tree.root_node, "type_identifier")
type_names = set()
for type_node in type_nodes:
typescript_analyzer._extract_type_names_from_node(type_node, source_code, type_names)
def test_extract_empty_node_set(typescript_analyzer):
"""Test that extraction on an empty set leaves it empty when no custom types found."""
# Parse code with only primitive types
source_code = b"let x: void;"
tree = typescript_analyzer.parser.parse(source_code)
# Find void type_identifier
def find_nodes_by_type(node, node_type):
results = []
if node.type == node_type:
results.append(node)
for child in node.children:
results.extend(find_nodes_by_type(child, node_type))
return results
type_nodes = find_nodes_by_type(tree.root_node, "type_identifier")
type_names = set()
for type_node in type_nodes:
typescript_analyzer._extract_type_names_from_node(type_node, source_code, type_names)
def test_extract_from_none_type_filtering(typescript_analyzer):
"""Test extraction when encountering 'null' and 'undefined' types."""
# Parse code with null and undefined
source_code = b"let x: null; let y: undefined;"
tree = typescript_analyzer.parser.parse(source_code)
def find_nodes_by_type(node, node_type):
results = []
if node.type == node_type:
results.append(node)
for child in node.children:
results.extend(find_nodes_by_type(child, node_type))
return results
type_nodes = find_nodes_by_type(tree.root_node, "type_identifier")
type_names = set()
for type_node in type_nodes:
typescript_analyzer._extract_type_names_from_node(type_node, source_code, type_names)
def test_extract_any_and_unknown_filtered(typescript_analyzer):
"""Test that 'any' and 'unknown' types are filtered."""
# Parse code with any and unknown
source_code = b"let x: any; let y: unknown;"
tree = typescript_analyzer.parser.parse(source_code)
def find_nodes_by_type(node, node_type):
results = []
if node.type == node_type:
results.append(node)
for child in node.children:
results.extend(find_nodes_by_type(child, node_type))
return results
type_nodes = find_nodes_by_type(tree.root_node, "type_identifier")
type_names = set()
for type_node in type_nodes:
typescript_analyzer._extract_type_names_from_node(type_node, source_code, type_names)
def test_extract_never_and_object_filtered(typescript_analyzer):
"""Test that 'never' and 'object' types are filtered."""
# Parse code with never and object
source_code = b"let x: never; let y: object;"
tree = typescript_analyzer.parser.parse(source_code)
def find_nodes_by_type(node, node_type):
results = []
if node.type == node_type:
results.append(node)
for child in node.children:
results.extend(find_nodes_by_type(child, node_type))
return results
type_nodes = find_nodes_by_type(tree.root_node, "type_identifier")
type_names = set()
for type_node in type_nodes:
typescript_analyzer._extract_type_names_from_node(type_node, source_code, type_names)
To test or edit this optimization locally git merge codeflash/optimize-pr1561-2026-02-20T18.54.58
Click to see suggested changes
| # Handle type identifiers (the actual type name references) | |
| if node.type == "type_identifier": | |
| type_name = self.get_node_text(node, source_bytes) | |
| # Skip primitive types | |
| if type_name not in ( | |
| "number", | |
| "string", | |
| "boolean", | |
| "void", | |
| "null", | |
| "undefined", | |
| "any", | |
| "never", | |
| "unknown", | |
| "object", | |
| "symbol", | |
| "bigint", | |
| ): | |
| type_names.add(type_name) | |
| return | |
| # Handle regular identifiers in type position (can happen in some contexts) | |
| if node.type == "identifier" and node.parent and node.parent.type in ("type_annotation", "generic_type"): | |
| type_name = self.get_node_text(node, source_bytes) | |
| if type_name not in ( | |
| "number", | |
| "string", | |
| "boolean", | |
| "void", | |
| "null", | |
| "undefined", | |
| "any", | |
| "never", | |
| "unknown", | |
| "object", | |
| "symbol", | |
| "bigint", | |
| ): | |
| type_names.add(type_name) | |
| return | |
| # Handle nested_type_identifier (e.g., Namespace.Type) | |
| if node.type == "nested_type_identifier": | |
| # Get the full qualified name | |
| node_type = node.type | |
| # Handle type identifiers (the actual type name references) | |
| if node_type == "type_identifier": | |
| type_name = self.get_node_text(node, source_bytes) | |
| # Skip primitive types | |
| if type_name not in self._PRIMITIVE_TYPES: | |
| type_names.add(type_name) | |
| return | |
| # Handle regular identifiers in type position (can happen in some contexts) | |
| if node_type == "identifier": | |
| parent = node.parent | |
| if parent and parent.type in ("type_annotation", "generic_type"): | |
| type_name = self.get_node_text(node, source_bytes) | |
| if type_name not in self._PRIMITIVE_TYPES: | |
| type_names.add(type_name) | |
| return | |
| # Handle nested_type_identifier (e.g., Namespace.Type) | |
| if node_type == "nested_type_identifier": | |
| # Get the full qualified name | |
| # Get the full qualified name |
| return TreeSitterAnalyzer(TreeSitterLanguage.TYPESCRIPT) | ||
| if suffix == ".tsx": | ||
| return TreeSitterAnalyzer(TreeSitterLanguage.TSX) | ||
| # Default to JavaScript for .js, .jsx, .mjs, .cjs | ||
| return TreeSitterAnalyzer(TreeSitterLanguage.JAVASCRIPT) |
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⚡️Codeflash found 11% (0.11x) speedup for get_analyzer_for_file in codeflash/languages/javascript/treesitter_utils.py
⏱️ Runtime : 361 microseconds → 324 microseconds (best of 148 runs)
📝 Explanation and details
The optimization achieves an 11% runtime improvement (from 361μs to 324μs) by eliminating repeated enum attribute lookups through pre-computed module-level constants.
Key Changes:
-
Module-level language constants: Three constants (
_LANG_TYPESCRIPT,_LANG_TSX,_LANG_JAVASCRIPT) are defined once at module load time, caching theTreeSitterLanguageenum members. -
Direct constant usage:
get_analyzer_for_file()now passes these pre-computed constants directly toTreeSitterAnalyzerinstead of accessing enum attributes on every call (e.g.,_LANG_TYPESCRIPTvsTreeSitterLanguage.TYPESCRIPT).
Why This Improves Runtime:
In Python, attribute access on enums involves dictionary lookups and additional overhead. By storing these enum values as module-level constants, we eliminate this repeated work. Each call to get_analyzer_for_file() avoids 1-3 enum attribute accesses (depending on which branch is taken), directly reducing the per-call overhead.
The line profiler shows that while individual line timings fluctuate (likely due to measurement variance), the aggregate function runtime consistently decreases across all test cases, with improvements ranging from 6-20% per test case.
Test Case Performance:
- TypeScript files (
.ts): 13-19% faster - benefits most since this is the most common branch - TSX files (
.tsx): 8-12% faster - second branch benefits similarly - JavaScript variants (
.js,.jsx,.mjs,.cjs): 7-20% faster - default case sees consistent gains - Edge cases (no extension, mixed case): 6-16% faster - all paths benefit from the optimization
This optimization is particularly valuable if get_analyzer_for_file() is called frequently in hot paths (e.g., during batch file processing or IDE integrations), as the savings compound with call volume. The 1000-file bulk tests show stable performance gains, confirming the optimization scales well with workload size.
✅ Correctness verification report:
| Test | Status |
|---|---|
| ⚙️ Existing Unit Tests | 🔘 None Found |
| 🌀 Generated Regression Tests | ✅ 346 Passed |
| ⏪ Replay Tests | 🔘 None Found |
| 🔎 Concolic Coverage Tests | 🔘 None Found |
| 📊 Tests Coverage | 100.0% |
🌀 Click to see Generated Regression Tests
from pathlib import Path
# imports
import pytest # used for our unit tests
# import the function and classes under test from the provided module path
from codeflash.languages.javascript.treesitter_utils import (
TreeSitterAnalyzer, TreeSitterLanguage, get_analyzer_for_file)
def test_ts_file_returns_typescript_analyzer():
# Create a Path for a .ts file
p = Path("example.ts")
# Call the function under test
codeflash_output = get_analyzer_for_file(p); analyzer = codeflash_output # 2.99μs -> 2.56μs (16.4% faster)
def test_tsx_and_js_variants_map_to_expected_languages():
# Check .tsx -> TSX
codeflash_output = get_analyzer_for_file(Path("component.tsx")).language # 2.83μs -> 2.52μs (12.3% faster)
# Default group: .js -> JavaScript
codeflash_output = get_analyzer_for_file(Path("script.js")).language # 1.41μs -> 1.25μs (12.9% faster)
# .jsx should also map to JavaScript
codeflash_output = get_analyzer_for_file(Path("view.jsx")).language # 1.13μs -> 1.05μs (7.60% faster)
# .mjs should map to JavaScript
codeflash_output = get_analyzer_for_file(Path("module.mjs")).language # 982ns -> 901ns (8.99% faster)
# .cjs should map to JavaScript
codeflash_output = get_analyzer_for_file(Path("common.cjs")).language # 941ns -> 851ns (10.6% faster)
def test_extension_case_insensitivity_and_multi_dot_filenames():
# Upper-case .TS should be recognized as TypeScript due to .lower() in implementation
codeflash_output = get_analyzer_for_file(Path("UPPER.TS")).language # 2.81μs -> 2.40μs (16.6% faster)
# Mixed-case .Tsx -> TSX
codeflash_output = get_analyzer_for_file(Path("mixed.TsX")).language # 1.37μs -> 1.24μs (10.5% faster)
# Multi-dot filename where final suffix is .ts
codeflash_output = get_analyzer_for_file(Path("archive.tar.ts")).language # 1.06μs -> 992ns (7.06% faster)
# File with no extension defaults to JavaScript
codeflash_output = get_analyzer_for_file(Path("Makefile")).language # 1.13μs -> 1.01μs (11.9% faster)
def test_unrecognized_and_trailing_dot_extensions_default_to_javascript():
# A file like ".bashrc" has suffix ".bashrc" which is unrecognized -> default JavaScript
codeflash_output = get_analyzer_for_file(Path(".bashrc")).language # 2.44μs -> 2.11μs (15.2% faster)
# A filename that ends with a dot has suffix '.' which is unrecognized -> default JavaScript
codeflash_output = get_analyzer_for_file(Path("weirdfile.")).language # 1.18μs -> 1.11μs (6.38% faster)
# Another unrecognized suffix (e.g., .py) -> default JavaScript
codeflash_output = get_analyzer_for_file(Path("script.py")).language # 1.36μs -> 1.24μs (9.65% faster)
def test_none_input_raises_attribute_error():
# Passing None should raise AttributeError because NoneType has no 'suffix' attribute.
with pytest.raises(AttributeError):
get_analyzer_for_file(None) # 2.54μs -> 2.73μs (6.61% slower)
def test_multiple_calls_return_distinct_instances_and_preserve_language():
# Calling the function twice for the same path should return two distinct analyzer instances
p = Path("dup.ts")
codeflash_output = get_analyzer_for_file(p); a1 = codeflash_output # 2.89μs -> 2.49μs (15.7% faster)
codeflash_output = get_analyzer_for_file(p); a2 = codeflash_output # 1.18μs -> 1.04μs (13.4% faster)
def test_large_scale_many_files_mapping_correctness_and_performance():
# Generate 1000 paths with a repeating pattern of extensions to exercise mapping at scale
n = 1000
paths = []
for i in range(n):
# Cycle through three extensions to create a varied set: .ts, .tsx, .js
if i % 3 == 0:
paths.append(Path(f"file_{i}.ts"))
elif i % 3 == 1:
paths.append(Path(f"file_{i}.tsx"))
else:
paths.append(Path(f"file_{i}.js"))
# Map each path to an analyzer and collect results
analyzers = [get_analyzer_for_file(p) for p in paths]
# Count how many analyzers were created for each language
counts = {
TreeSitterLanguage.TYPESCRIPT: 0,
TreeSitterLanguage.TSX: 0,
TreeSitterLanguage.JAVASCRIPT: 0,
}
for a in analyzers:
counts[a.language] += 1
# Compute expected counts programmatically so the test remains correct for n changes
expected_counts = {
TreeSitterLanguage.TYPESCRIPT: sum(1 for i in range(n) if i % 3 == 0),
TreeSitterLanguage.TSX: sum(1 for i in range(n) if i % 3 == 1),
TreeSitterLanguage.JAVASCRIPT: sum(1 for i in range(n) if i % 3 == 2),
}
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.from pathlib import Path
# imports
import pytest
from codeflash.languages.javascript.treesitter_utils import (
TreeSitterAnalyzer, TreeSitterLanguage, get_analyzer_for_file)
def test_typescript_file_returns_typescript_analyzer():
"""Verify that a .ts file returns an analyzer configured for TypeScript."""
file_path = Path("example.ts")
codeflash_output = get_analyzer_for_file(file_path); analyzer = codeflash_output # 2.99μs -> 2.52μs (18.3% faster)
def test_tsx_file_returns_tsx_analyzer():
"""Verify that a .tsx file returns an analyzer configured for TSX."""
file_path = Path("component.tsx")
codeflash_output = get_analyzer_for_file(file_path); analyzer = codeflash_output # 2.96μs -> 2.67μs (10.9% faster)
def test_javascript_file_returns_javascript_analyzer():
"""Verify that a .js file returns an analyzer configured for JavaScript."""
file_path = Path("script.js")
codeflash_output = get_analyzer_for_file(file_path); analyzer = codeflash_output # 2.96μs -> 2.46μs (20.3% faster)
def test_jsx_file_returns_javascript_analyzer():
"""Verify that a .jsx file defaults to JavaScript analyzer."""
file_path = Path("component.jsx")
codeflash_output = get_analyzer_for_file(file_path); analyzer = codeflash_output # 2.79μs -> 2.48μs (12.1% faster)
def test_mjs_file_returns_javascript_analyzer():
"""Verify that a .mjs (ES module) file defaults to JavaScript analyzer."""
file_path = Path("module.mjs")
codeflash_output = get_analyzer_for_file(file_path); analyzer = codeflash_output # 2.77μs -> 2.44μs (13.1% faster)
def test_cjs_file_returns_javascript_analyzer():
"""Verify that a .cjs (CommonJS) file defaults to JavaScript analyzer."""
file_path = Path("module.cjs")
codeflash_output = get_analyzer_for_file(file_path); analyzer = codeflash_output # 2.62μs -> 2.46μs (6.92% faster)
def test_unknown_extension_returns_javascript_analyzer():
"""Verify that unknown extensions default to JavaScript analyzer."""
file_path = Path("file.xyz")
codeflash_output = get_analyzer_for_file(file_path); analyzer = codeflash_output # 2.69μs -> 2.38μs (13.1% faster)
def test_analyzer_has_language_attribute():
"""Verify that returned analyzer has a language attribute."""
file_path = Path("test.ts")
codeflash_output = get_analyzer_for_file(file_path); analyzer = codeflash_output # 2.94μs -> 2.46μs (19.1% faster)
def test_uppercase_ts_extension():
"""Verify that uppercase .TS extension is handled correctly."""
file_path = Path("example.TS")
codeflash_output = get_analyzer_for_file(file_path); analyzer = codeflash_output # 2.81μs -> 2.47μs (13.4% faster)
def test_uppercase_tsx_extension():
"""Verify that uppercase .TSX extension is handled correctly."""
file_path = Path("component.TSX")
codeflash_output = get_analyzer_for_file(file_path); analyzer = codeflash_output # 2.67μs -> 2.46μs (8.52% faster)
def test_uppercase_js_extension():
"""Verify that uppercase .JS extension is handled correctly."""
file_path = Path("script.JS")
codeflash_output = get_analyzer_for_file(file_path); analyzer = codeflash_output # 2.83μs -> 2.54μs (11.0% faster)
def test_mixed_case_ts_extension():
"""Verify that mixed-case .Ts extension is handled correctly."""
file_path = Path("example.Ts")
codeflash_output = get_analyzer_for_file(file_path); analyzer = codeflash_output # 2.86μs -> 2.48μs (15.4% faster)
def test_mixed_case_tsx_extension():
"""Verify that mixed-case .TsX extension is handled correctly."""
file_path = Path("component.TsX")
codeflash_output = get_analyzer_for_file(file_path); analyzer = codeflash_output # 2.79μs -> 2.50μs (11.2% faster)
def test_file_with_no_extension():
"""Verify that a file with no extension defaults to JavaScript."""
file_path = Path("Dockerfile")
codeflash_output = get_analyzer_for_file(file_path); analyzer = codeflash_output # 2.52μs -> 2.21μs (13.6% faster)
def test_file_with_dot_but_no_extension():
"""Verify that a file ending with a dot defaults to JavaScript."""
file_path = Path("file.")
codeflash_output = get_analyzer_for_file(file_path); analyzer = codeflash_output # 2.44μs -> 2.17μs (12.0% faster)
def test_nested_path_ts_file():
"""Verify that nested paths are handled correctly."""
file_path = Path("src/components/utils/helper.ts")
codeflash_output = get_analyzer_for_file(file_path); analyzer = codeflash_output # 2.97μs -> 2.58μs (15.2% faster)
def test_nested_path_tsx_file():
"""Verify that nested paths with tsx are handled correctly."""
file_path = Path("src/components/MyComponent.tsx")
codeflash_output = get_analyzer_for_file(file_path); analyzer = codeflash_output # 2.83μs -> 2.56μs (11.0% faster)
def test_deeply_nested_path():
"""Verify that deeply nested paths are handled correctly."""
file_path = Path("a/b/c/d/e/f/g/h/i/j/script.ts")
codeflash_output = get_analyzer_for_file(file_path); analyzer = codeflash_output # 2.83μs -> 2.42μs (17.0% faster)
def test_hidden_typescript_file():
"""Verify that hidden TypeScript files are handled correctly."""
file_path = Path(".hidden.ts")
codeflash_output = get_analyzer_for_file(file_path); analyzer = codeflash_output # 2.79μs -> 2.43μs (14.8% faster)
def test_file_with_multiple_dots():
"""Verify that files with multiple dots in name use the last extension."""
file_path = Path("config.test.ts")
codeflash_output = get_analyzer_for_file(file_path); analyzer = codeflash_output # 2.81μs -> 2.42μs (15.7% faster)
def test_file_with_multiple_dots_js():
"""Verify that files with multiple dots use the last extension for .js."""
file_path = Path("utils.min.js")
codeflash_output = get_analyzer_for_file(file_path); analyzer = codeflash_output # 2.83μs -> 2.45μs (15.5% faster)
def test_file_with_special_characters_in_name():
"""Verify that files with special characters in name are handled."""
file_path = Path("my-component.@latest.tsx")
codeflash_output = get_analyzer_for_file(file_path); analyzer = codeflash_output # 2.91μs -> 2.52μs (15.5% faster)
def test_typescript_file_with_windows_path():
"""Verify that Windows-style paths are handled correctly."""
file_path = Path("src\\components\\utils.ts")
codeflash_output = get_analyzer_for_file(file_path); analyzer = codeflash_output # 2.90μs -> 2.46μs (17.5% faster)
def test_multiple_analyzers_for_same_ts_file():
"""Verify that multiple calls create independent analyzer instances."""
file_path = Path("example.ts")
# Create multiple analyzers
analyzers = [get_analyzer_for_file(file_path) for _ in range(100)]
def test_many_different_file_extensions():
"""Verify correct language assignment for many different files."""
test_cases = [
(Path("file1.ts"), TreeSitterLanguage.TYPESCRIPT),
(Path("file2.tsx"), TreeSitterLanguage.TSX),
(Path("file3.js"), TreeSitterLanguage.JAVASCRIPT),
(Path("file4.jsx"), TreeSitterLanguage.JAVASCRIPT),
(Path("file5.mjs"), TreeSitterLanguage.JAVASCRIPT),
(Path("file6.cjs"), TreeSitterLanguage.JAVASCRIPT),
(Path("file7.unknown"), TreeSitterLanguage.JAVASCRIPT),
]
# Test each case
results = [(path, get_analyzer_for_file(path)) for path, _ in test_cases]
# Verify each result
for i, (path, expected_language) in enumerate(test_cases):
actual_analyzer = results[i][1]
def test_thousand_typescript_files():
"""Verify performance handling of many TypeScript files."""
# Create 1000 analyzer instances for TypeScript files
analyzers = [
get_analyzer_for_file(Path(f"file_{i}.ts"))
for i in range(1000)
]
def test_thousand_mixed_extensions():
"""Verify performance with mixed extensions in 1000 files."""
extensions = [".ts", ".tsx", ".js", ".jsx", ".mjs", ".cjs"]
expected_languages = [
TreeSitterLanguage.TYPESCRIPT,
TreeSitterLanguage.TSX,
TreeSitterLanguage.JAVASCRIPT,
TreeSitterLanguage.JAVASCRIPT,
TreeSitterLanguage.JAVASCRIPT,
TreeSitterLanguage.JAVASCRIPT,
]
# Create 1000 files with rotating extensions
analyzers = [
get_analyzer_for_file(Path(f"file_{i}{extensions[i % 6]}"))
for i in range(1000)
]
# Verify each has correct language
for i, analyzer in enumerate(analyzers):
expected = expected_languages[i % 6]
def test_many_nested_paths():
"""Verify performance with 1000 deeply nested paths."""
# Create paths with varying nesting depths
analyzers = [
get_analyzer_for_file(
Path("/".join([f"dir_{j}" for j in range(i % 10 + 1)]) + "/file.ts")
)
for i in range(1000)
]
def test_case_variation_performance():
"""Verify performance with 1000 files of case-varied extensions."""
# Test case variations at scale
case_variations = [".ts", ".TS", ".Ts", ".tS"]
analyzers = [
get_analyzer_for_file(Path(f"file_{i}{case_variations[i % 4]}"))
for i in range(1000)
]
def test_sequential_different_file_types():
"""Verify correct switching between different file types at scale."""
# Alternate between different file types
file_types = [
(Path("file.ts"), TreeSitterLanguage.TYPESCRIPT),
(Path("file.tsx"), TreeSitterLanguage.TSX),
(Path("file.js"), TreeSitterLanguage.JAVASCRIPT),
]
analyzers = []
for i in range(300):
path, expected_language = file_types[i % 3]
codeflash_output = get_analyzer_for_file(path); analyzer = codeflash_output # 267μs -> 241μs (10.9% faster)
analyzers.append(analyzer)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.To test or edit this optimization locally git merge codeflash/optimize-pr1561-2026-02-20T19.10.53
| return TreeSitterAnalyzer(TreeSitterLanguage.TYPESCRIPT) | |
| if suffix == ".tsx": | |
| return TreeSitterAnalyzer(TreeSitterLanguage.TSX) | |
| # Default to JavaScript for .js, .jsx, .mjs, .cjs | |
| return TreeSitterAnalyzer(TreeSitterLanguage.JAVASCRIPT) | |
| return TreeSitterAnalyzer(_LANG_TYPESCRIPT) | |
| if suffix == ".tsx": | |
| return TreeSitterAnalyzer(_LANG_TSX) | |
| # Default to JavaScript for .js, .jsx, .mjs, .cjs | |
| return TreeSitterAnalyzer(_LANG_JAVASCRIPT) | |
| _LANG_TYPESCRIPT = TreeSitterLanguage.TYPESCRIPT | |
| _LANG_TSX = TreeSitterLanguage.TSX | |
| _LANG_JAVASCRIPT = TreeSitterLanguage.JAVASCRIPT |
| # Check render count reduction | ||
| count_reduction = (original_render_count - optimized_render_count) / original_render_count | ||
| count_improved = count_reduction >= MIN_RENDER_COUNT_REDUCTION_PCT | ||
|
|
||
| # Check render duration reduction | ||
| duration_improved = False | ||
| if original_render_duration > 0: | ||
| duration_gain = (original_render_duration - optimized_render_duration) / original_render_duration | ||
| duration_improved = duration_gain > MIN_IMPROVEMENT_THRESHOLD | ||
|
|
||
| # Check if this is the best candidate so far | ||
| is_best = best_render_count_until_now is None or optimized_render_count <= best_render_count_until_now | ||
|
|
||
| return (count_improved or duration_improved) and is_best |
There was a problem hiding this comment.
⚡️Codeflash found 23% (0.23x) speedup for render_efficiency_critic in codeflash/result/critic.py
⏱️ Runtime : 1.88 milliseconds → 1.53 milliseconds (best of 150 runs)
📝 Explanation and details
The optimized code achieves a 22% runtime improvement by restructuring the conditional logic to enable early exits and reduce unnecessary computations.
Key Optimizations
1. Early Rejection Check
The optimization moves the is_best check to the front and inverts it to exit immediately when optimized_render_count > best_render_count_until_now. This is critical because:
- The line profiler shows this check eliminates 4,227 unnecessary evaluations (only 3 failures occur early)
- In the original code, all conditions were evaluated before the final AND operation
- The test results demonstrate massive speedups (84-96%) for cases that fail the best candidate check (e.g.,
test_optimized_count_worse_than_best_is_rejected,test_render_count_increased)
2. Early Success Returns
After checking render count reduction, the code now returns True immediately if the threshold is met, skipping duration calculations entirely:
- Line profiler shows 2,991 early returns after the count check
- This eliminates expensive division operations for duration_gain calculation in 71% of successful cases
- Tests like
test_only_count_improved_is_acceptedshow 46.6% speedup, benefiting directly from this optimization
3. Elimination of Intermediate Boolean Variables
The original code stored count_improved, duration_improved, and is_best as variables, then combined them in a final boolean expression. The optimized version:
- Removes these allocations (saves ~1.4μs in line profiler across ~4,230 hits)
- Directly returns based on conditions, reducing memory operations
Performance Impact Analysis
Based on the function_references, render_efficiency_critic is called in React optimization workflows where it evaluates each optimization candidate. The test suite shows the function is called thousands of times in typical workloads (test_large_scale_many_iterations_of_evaluations runs 1,000 evaluations).
The optimization is particularly effective for:
- Rejection scenarios (90-96% faster): When candidates fail the best count check, they exit in ~700-900ns vs ~1.4-1.7μs
- Count-only improvements (29-47% faster): Cases where render count reduction alone is sufficient
- Batch evaluation workloads (19-28% faster): The large-scale tests show consistent 20-30% improvements when evaluating hundreds of candidates
The early exit strategy means that in a typical optimization pipeline where multiple candidates are evaluated against a best-so-far baseline, failed candidates are rejected ~2x faster, significantly reducing overall evaluation time.
✅ Correctness verification report:
| Test | Status |
|---|---|
| ⚙️ Existing Unit Tests | 🔘 None Found |
| 🌀 Generated Regression Tests | ✅ 4231 Passed |
| ⏪ Replay Tests | 🔘 None Found |
| 🔎 Concolic Coverage Tests | 🔘 None Found |
| 📊 Tests Coverage | 100.0% |
🌀 Click to see Generated Regression Tests
import pytest
from codeflash.code_utils.config_consts import MIN_IMPROVEMENT_THRESHOLD
from codeflash.result.critic import render_efficiency_critic
def test_render_count_reduced_by_20_percent_is_accepted():
"""Test that a 20% render count reduction is accepted when it's the best so far."""
# 100 renders reduced to 80 renders = 20% reduction
codeflash_output = render_efficiency_critic(
original_render_count=100,
optimized_render_count=80,
original_render_duration=1.0,
optimized_render_duration=1.0,
); result = codeflash_output # 1.56μs -> 1.23μs (26.8% faster)
def test_render_count_reduced_by_more_than_20_percent_is_accepted():
"""Test that >20% render count reduction is accepted."""
# 100 renders reduced to 70 renders = 30% reduction
codeflash_output = render_efficiency_critic(
original_render_count=100,
optimized_render_count=70,
original_render_duration=1.0,
optimized_render_duration=1.0,
); result = codeflash_output # 1.51μs -> 1.15μs (31.2% faster)
def test_render_count_reduced_by_less_than_20_percent_without_duration_gain():
"""Test that <20% render count reduction without duration improvement is rejected."""
# 100 renders reduced to 85 renders = 15% reduction (less than 20%)
codeflash_output = render_efficiency_critic(
original_render_count=100,
optimized_render_count=85,
original_render_duration=1.0,
optimized_render_duration=1.0,
); result = codeflash_output # 1.48μs -> 1.43μs (3.42% faster)
def test_render_duration_reduced_by_min_improvement_threshold_is_accepted():
"""Test that render duration reduction >= MIN_IMPROVEMENT_THRESHOLD is accepted."""
# Assuming MIN_IMPROVEMENT_THRESHOLD is around 0.1-0.15, test with duration improvement
original_duration = 1.0
# Calculate optimized duration to meet MIN_IMPROVEMENT_THRESHOLD
optimized_duration = original_duration * (1 - MIN_IMPROVEMENT_THRESHOLD)
codeflash_output = render_efficiency_critic(
original_render_count=100,
optimized_render_count=95, # Only 5% reduction, below 20%
original_render_duration=original_duration,
optimized_render_duration=optimized_duration,
); result = codeflash_output # 1.41μs -> 1.27μs (10.9% faster)
def test_zero_original_render_count_returns_false():
"""Test that zero original render count always returns False."""
codeflash_output = render_efficiency_critic(
original_render_count=0,
optimized_render_count=0,
original_render_duration=1.0,
optimized_render_duration=0.5,
); result = codeflash_output # 731ns -> 691ns (5.79% faster)
def test_best_render_count_until_now_none_accepts_improvement():
"""Test that no previous best (None) allows acceptance of valid improvement."""
codeflash_output = render_efficiency_critic(
original_render_count=100,
optimized_render_count=80,
original_render_duration=1.0,
optimized_render_duration=1.0,
best_render_count_until_now=None,
); result = codeflash_output # 1.57μs -> 1.25μs (25.6% faster)
def test_optimized_count_equals_best_is_accepted():
"""Test that optimized count equal to best is accepted."""
codeflash_output = render_efficiency_critic(
original_render_count=100,
optimized_render_count=80,
original_render_duration=1.0,
optimized_render_duration=1.0,
best_render_count_until_now=80,
); result = codeflash_output # 1.53μs -> 1.12μs (36.6% faster)
def test_optimized_count_better_than_best_is_accepted():
"""Test that optimized count better than previous best is accepted."""
codeflash_output = render_efficiency_critic(
original_render_count=100,
optimized_render_count=70,
original_render_duration=1.0,
optimized_render_duration=1.0,
best_render_count_until_now=80,
); result = codeflash_output # 1.50μs -> 1.10μs (36.3% faster)
def test_optimized_count_worse_than_best_is_rejected():
"""Test that optimized count worse than previous best is rejected."""
codeflash_output = render_efficiency_critic(
original_render_count=100,
optimized_render_count=85,
original_render_duration=1.0,
optimized_render_duration=0.5, # Even with duration improvement
best_render_count_until_now=80,
); result = codeflash_output # 1.49μs -> 761ns (96.2% faster)
def test_both_count_and_duration_improved_is_accepted():
"""Test that improvement in both count and duration is accepted."""
codeflash_output = render_efficiency_critic(
original_render_count=100,
optimized_render_count=75, # 25% reduction
original_render_duration=1.0,
optimized_render_duration=0.8, # 20% reduction
); result = codeflash_output # 1.46μs -> 1.13μs (29.2% faster)
def test_only_count_improved_is_accepted():
"""Test that improvement in only count is accepted."""
codeflash_output = render_efficiency_critic(
original_render_count=100,
optimized_render_count=75, # 25% reduction
original_render_duration=1.0,
optimized_render_duration=1.0, # No improvement
); result = codeflash_output # 1.51μs -> 1.03μs (46.6% faster)
def test_only_duration_improved_is_accepted():
"""Test that improvement in only duration is accepted."""
codeflash_output = render_efficiency_critic(
original_render_count=100,
optimized_render_count=100, # No count improvement
original_render_duration=1.0,
optimized_render_duration=1.0 * (1 - MIN_IMPROVEMENT_THRESHOLD), # Meets threshold
); result = codeflash_output # 1.45μs -> 1.39μs (4.31% faster)
def test_exact_20_percent_render_count_reduction_boundary():
"""Test exact 20% render count reduction at boundary."""
# 100 renders reduced to 80 renders = exactly 20% reduction
codeflash_output = render_efficiency_critic(
original_render_count=100,
optimized_render_count=80,
original_render_duration=1.0,
optimized_render_duration=1.0,
); result = codeflash_output # 1.53μs -> 1.12μs (36.6% faster)
def test_just_below_20_percent_render_count_reduction_boundary():
"""Test just below 20% render count reduction is rejected."""
# 100 renders reduced to 81 renders = 19% reduction (just below 20%)
codeflash_output = render_efficiency_critic(
original_render_count=100,
optimized_render_count=81,
original_render_duration=1.0,
optimized_render_duration=1.0,
); result = codeflash_output # 1.47μs -> 1.41μs (4.18% faster)
def test_very_large_render_counts():
"""Test with very large render counts."""
# 1,000,000 renders reduced to 800,000 renders = 20% reduction
codeflash_output = render_efficiency_critic(
original_render_count=1_000_000,
optimized_render_count=800_000,
original_render_duration=1.0,
optimized_render_duration=1.0,
); result = codeflash_output # 1.48μs -> 1.17μs (26.5% faster)
def test_single_render_count():
"""Test with render count of 1."""
# 1 render reduced to 0 renders = 100% reduction
codeflash_output = render_efficiency_critic(
original_render_count=1,
optimized_render_count=0,
original_render_duration=1.0,
optimized_render_duration=1.0,
); result = codeflash_output # 1.47μs -> 1.11μs (32.4% faster)
def test_render_count_increased():
"""Test when render count is increased (negative improvement)."""
# 100 renders increased to 120 renders = -20% reduction
codeflash_output = render_efficiency_critic(
original_render_count=100,
optimized_render_count=120,
original_render_duration=1.0,
optimized_render_duration=0.5, # Even with duration improvement
best_render_count_until_now=100,
); result = codeflash_output # 1.68μs -> 882ns (90.9% faster)
def test_zero_original_duration():
"""Test with zero original render duration."""
# When original_render_duration is 0, duration_improved should be False
codeflash_output = render_efficiency_critic(
original_render_count=100,
optimized_render_count=80, # 20% reduction (meets threshold)
original_render_duration=0.0,
optimized_render_duration=0.0,
); result = codeflash_output # 1.28μs -> 1.16μs (10.4% faster)
def test_zero_optimized_duration():
"""Test with zero optimized render duration."""
codeflash_output = render_efficiency_critic(
original_render_count=100,
optimized_render_count=95, # 5% reduction (below 20%)
original_render_duration=1.0,
optimized_render_duration=0.0, # 100% duration improvement
); result = codeflash_output # 1.48μs -> 1.44μs (2.77% faster)
def test_negative_duration_not_realistic_but_handled():
"""Test behavior with negative durations (edge case)."""
# While not realistic, the function should handle this mathematically
codeflash_output = render_efficiency_critic(
original_render_count=100,
optimized_render_count=80, # 20% reduction
original_render_duration=1.0,
optimized_render_duration=-1.0, # Negative duration
); result = codeflash_output # 1.43μs -> 1.12μs (27.7% faster)
def test_very_small_duration_values():
"""Test with very small duration values (microseconds)."""
codeflash_output = render_efficiency_critic(
original_render_count=100,
optimized_render_count=85, # 15% reduction (below 20%)
original_render_duration=0.000001, # 1 microsecond
optimized_render_duration=0.0000005, # 50% reduction
); result = codeflash_output # 1.47μs -> 1.42μs (3.59% faster)
def test_very_large_duration_values():
"""Test with very large duration values (many seconds)."""
codeflash_output = render_efficiency_critic(
original_render_count=100,
optimized_render_count=85, # 15% reduction (below 20%)
original_render_duration=10000.0, # 10,000 seconds
optimized_render_duration=10000.0 * (1 - MIN_IMPROVEMENT_THRESHOLD),
); result = codeflash_output # 1.42μs -> 1.27μs (11.9% faster)
def test_best_render_count_zero():
"""Test when best_render_count_until_now is 0."""
codeflash_output = render_efficiency_critic(
original_render_count=100,
optimized_render_count=1, # Better than original but can't beat 0
original_render_duration=1.0,
optimized_render_duration=1.0,
best_render_count_until_now=0,
); result = codeflash_output # 1.55μs -> 842ns (84.4% faster)
def test_optimized_equals_original_count_without_best():
"""Test when optimized count equals original count without previous best."""
# 100 renders stays at 100 renders = 0% reduction
codeflash_output = render_efficiency_critic(
original_render_count=100,
optimized_render_count=100,
original_render_duration=1.0,
optimized_render_duration=1.0,
); result = codeflash_output # 1.43μs -> 1.37μs (4.30% faster)
def test_float_render_counts():
"""Test with float values for render counts."""
# 100.5 renders reduced to 80.4 renders = ~20% reduction
codeflash_output = render_efficiency_critic(
original_render_count=100.5,
optimized_render_count=80.4,
original_render_duration=1.0,
optimized_render_duration=1.0,
); result = codeflash_output # 1.57μs -> 1.47μs (6.72% faster)
def test_float_durations():
"""Test with float values for durations."""
codeflash_output = render_efficiency_critic(
original_render_count=100,
optimized_render_count=85, # 15% reduction (below 20%)
original_render_duration=1.5,
optimized_render_duration=1.5 * (1 - MIN_IMPROVEMENT_THRESHOLD),
); result = codeflash_output # 1.42μs -> 1.33μs (6.83% faster)
def test_both_improvements_at_exact_threshold():
"""Test count at threshold and duration improvement meets threshold."""
codeflash_output = render_efficiency_critic(
original_render_count=100,
optimized_render_count=80, # Exactly 20%
original_render_duration=1.0,
optimized_render_duration=1.0 * (1 - MIN_IMPROVEMENT_THRESHOLD),
); result = codeflash_output # 1.40μs -> 1.04μs (34.6% faster)
def test_negative_render_counts_not_realistic():
"""Test with negative render counts (edge case, not realistic)."""
codeflash_output = render_efficiency_critic(
original_render_count=100,
optimized_render_count=-10, # Negative optimized count
original_render_duration=1.0,
optimized_render_duration=1.0,
); result = codeflash_output # 1.50μs -> 1.12μs (34.0% faster)
def test_very_precise_duration_reduction_just_below_threshold():
"""Test duration reduction just below MIN_IMPROVEMENT_THRESHOLD."""
# Create a duration improvement just below threshold
threshold_minus_epsilon = MIN_IMPROVEMENT_THRESHOLD - 0.0001
codeflash_output = render_efficiency_critic(
original_render_count=100,
optimized_render_count=85, # 15% reduction (below 20%)
original_render_duration=1.0,
optimized_render_duration=1.0 * (1 - threshold_minus_epsilon),
); result = codeflash_output # 1.43μs -> 1.29μs (10.8% faster)
def test_very_precise_duration_reduction_just_above_threshold():
"""Test duration reduction just above MIN_IMPROVEMENT_THRESHOLD."""
# Create a duration improvement just above threshold
threshold_plus_epsilon = MIN_IMPROVEMENT_THRESHOLD + 0.0001
codeflash_output = render_efficiency_critic(
original_render_count=100,
optimized_render_count=85, # 15% reduction (below 20%)
original_render_duration=1.0,
optimized_render_duration=1.0 * (1 - threshold_plus_epsilon),
); result = codeflash_output # 1.43μs -> 1.35μs (5.99% faster)
def test_large_scale_many_iterations_of_evaluations():
"""Test performance with 1000 evaluations to ensure no performance regression."""
# Run 1000 evaluations with varying parameters
for i in range(1000):
original_count = 1000 + i
optimized_count = int(original_count * 0.8) # 20% reduction
codeflash_output = render_efficiency_critic(
original_render_count=original_count,
optimized_render_count=optimized_count,
original_render_duration=1.0 + i * 0.001,
optimized_render_duration=1.0 + i * 0.001,
); result = codeflash_output # 422μs -> 329μs (28.4% faster)
def test_large_scale_very_large_render_counts():
"""Test with very large render count values."""
# Test with counts in the billions
codeflash_output = render_efficiency_critic(
original_render_count=1_000_000_000,
optimized_render_count=800_000_000, # 20% reduction
original_render_duration=1.0,
optimized_render_duration=1.0,
); result = codeflash_output # 1.75μs -> 1.32μs (32.6% faster)
def test_large_scale_many_best_candidates():
"""Test evaluation with 1000 different optimization attempts."""
best_so_far = 1000
# Simulate 1000 optimization attempts, each trying to beat the previous best
for attempt in range(1000):
optimized_count = best_so_far - attempt - 1 # Each one is better
codeflash_output = render_efficiency_critic(
original_render_count=1000,
optimized_render_count=optimized_count,
original_render_duration=1.0,
optimized_render_duration=1.0,
best_render_count_until_now=best_so_far,
); result = codeflash_output # 447μs -> 355μs (26.0% faster)
# Except the first ones which don't meet 20% threshold
if attempt < 200: # First 200 attempts stay above 800 (less than 20% reduction)
expected = False
else:
expected = True
if result:
best_so_far = optimized_count
def test_large_scale_fluctuating_improvements():
"""Test 1000 evaluations with fluctuating improvement levels."""
results = []
# Alternate between good and bad improvements
for i in range(1000):
if i % 2 == 0:
# Good improvement: 20% count reduction
optimized_count = int(1000 * 0.8)
expected = True
else:
# Bad improvement: 5% count reduction, no duration improvement
optimized_count = int(1000 * 0.95)
expected = False
codeflash_output = render_efficiency_critic(
original_render_count=1000,
optimized_render_count=optimized_count,
original_render_duration=1.0,
optimized_render_duration=1.0,
); result = codeflash_output # 441μs -> 370μs (19.1% faster)
results.append(result)
# Verify pattern: even indices should be True, odd indices should be False
for i in range(1000):
if i % 2 == 0:
pass
else:
pass
def test_large_scale_progressive_best_count_reduction():
"""Test 1000 progressive optimizations where each is better than the last."""
best_count = 10000
for attempt in range(100): # 100 progressive improvements
# Each improvement gets 1% better render count
new_optimized_count = int(best_count * (1 - 0.01))
# Only accept if it's a 20% improvement from original
original_count = 1000
improvement_pct = (original_count - new_optimized_count) / original_count
codeflash_output = render_efficiency_critic(
original_render_count=original_count,
optimized_render_count=new_optimized_count,
original_render_duration=1.0,
optimized_render_duration=1.0,
best_render_count_until_now=best_count if attempt > 0 else None,
); result = codeflash_output # 46.8μs -> 42.2μs (10.9% faster)
best_count = new_optimized_count
def test_large_scale_random_like_sequence():
"""Test with 500 different optimization scenarios."""
# Test various combinations without mocks
test_cases = []
# Generate 500 test cases with different parameters
for i in range(500):
original_render = 100 + i
# Mix between different reduction percentages
reduction_pct = 0.15 + (i % 20) * 0.01 # 15% to 34% reductions
optimized_render = int(original_render * (1 - reduction_pct))
# Mix between different durations
original_duration = 0.1 * (i % 100)
duration_improvement = 0.05 + (i % 20) * 0.01
optimized_duration = original_duration * (1 - duration_improvement)
codeflash_output = render_efficiency_critic(
original_render_count=original_render,
optimized_render_count=optimized_render,
original_render_duration=original_duration,
optimized_render_duration=optimized_duration,
); result = codeflash_output # 215μs -> 175μs (23.0% faster)
test_cases.append(result)
def test_large_scale_duration_only_improvements():
"""Test 100 cases with duration improvement but no count improvement."""
for i in range(100):
original_duration = 1.0 + i * 0.01
# Each has different duration improvement level
duration_improvement_pct = MIN_IMPROVEMENT_THRESHOLD + i * 0.001
optimized_duration = original_duration * (1 - duration_improvement_pct)
codeflash_output = render_efficiency_critic(
original_render_count=100,
optimized_render_count=100, # No count improvement
original_render_duration=original_duration,
optimized_render_duration=optimized_duration,
); result = codeflash_output # 44.5μs -> 40.8μs (9.21% faster)
def test_large_scale_boundary_near_20_percent():
"""Test 500 cases near the 20% boundary."""
results_near_boundary = []
for i in range(500):
# Create values oscillating around the 20% boundary
# Values from 19.5% to 20.5% reduction
reduction_pct = 0.195 + (i / 500.0) * 0.01
optimized_count = int(1000 * (1 - reduction_pct))
codeflash_output = render_efficiency_critic(
original_render_count=1000,
optimized_render_count=optimized_count,
original_render_duration=1.0,
optimized_render_duration=1.0,
); result = codeflash_output # 215μs -> 178μs (20.6% faster)
results_near_boundary.append((reduction_pct, result))
# Verify that results change from False to True as reduction increases
false_results = [r for r in results_near_boundary if r[1] is False]
true_results = [r for r in results_near_boundary if r[1] is True]
# False results should have lower reduction percentages
max_false_reduction = max([r[0] for r in false_results]) if false_results else 0
min_true_reduction = min([r[0] for r in true_results]) if true_results else 1
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.To test or edit this optimization locally git merge codeflash/optimize-pr1561-2026-02-20T19.18.11
Click to see suggested changes
| # Check render count reduction | |
| count_reduction = (original_render_count - optimized_render_count) / original_render_count | |
| count_improved = count_reduction >= MIN_RENDER_COUNT_REDUCTION_PCT | |
| # Check render duration reduction | |
| duration_improved = False | |
| if original_render_duration > 0: | |
| duration_gain = (original_render_duration - optimized_render_duration) / original_render_duration | |
| duration_improved = duration_gain > MIN_IMPROVEMENT_THRESHOLD | |
| # Check if this is the best candidate so far | |
| is_best = best_render_count_until_now is None or optimized_render_count <= best_render_count_until_now | |
| return (count_improved or duration_improved) and is_best | |
| # Check if this is the best candidate so far | |
| if best_render_count_until_now is not None and optimized_render_count > best_render_count_until_now: | |
| return False | |
| # Check render count reduction | |
| count_reduction = (original_render_count - optimized_render_count) / original_render_count | |
| if count_reduction >= MIN_RENDER_COUNT_REDUCTION_PCT: | |
| return True | |
| # Check render duration reduction | |
| if original_render_duration > 0: | |
| duration_gain = (original_render_duration - optimized_render_duration) / original_render_duration | |
| if duration_gain > MIN_IMPROVEMENT_THRESHOLD: | |
| return True | |
| return False |
…2026-02-20T15.27.15 ⚡️ Speed up method `TreeSitterAnalyzer.is_function_exported` by 866% in PR #1561 (`add/support_react`)
|
This PR is now faster! 🚀 @claude[bot] accepted my optimizations from: |
…2026-02-20T13.45.13 ⚡️ Speed up method `JavaScriptSupport._extract_types_from_definition` by 1,618% in PR #1561 (`add/support_react`)
|
This PR is now faster! 🚀 @claude[bot] accepted my optimizations from: |
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