⚡️ Speed up method `PrComment.to_json` by 28% in PR #1199 (`omni-java`) by codeflash-ai[bot] · Pull Request #1624 · codeflash-ai/codeflash

codeflash-ai · 2026-02-20T21:40:22Z

⚡️ This pull request contains optimizations for PR #1199

If you approve this dependent PR, these changes will be merged into the original PR branch omni-java.

This PR will be automatically closed if the original PR is merged.

📄 28% (0.28x) speedup for `PrComment.to_json` in `codeflash/github/PrComment.py`

⏱️ Runtime : 5.96 milliseconds → 4.64 milliseconds (best of 54 runs)

📝 Explanation and details

The optimization achieves a 28% runtime improvement (5.96ms → 4.64ms) by adding @lru_cache(maxsize=1024) to the humanize_runtime function in time_utils.py.

Why This Works:

The humanize_runtime function performs expensive string formatting operations - converting nanosecond timestamps to human-readable formats with proper unit selection and decimal place formatting. Looking at the line profiler data:

Original: humanize_runtime total time was 6.86ms across 2,058 calls (~3.3μs per call)
Optimized: Eliminated after caching, reducing to_json overhead from ~6.48ms + ~5.95ms = ~12.43ms for two humanize_runtime calls down to ~1.69ms + ~1.48ms = ~3.17ms

Key Performance Factors:

Repeated conversions: The function is called twice per to_json invocation (for best_runtime and original_runtime), and test results show it's often called with the same values repeatedly (e.g., in test_multiple_to_json_calls_are_deterministic with 1000 iterations, the same runtimes are formatted repeatedly)
Expensive operations being cached:
- Multiple floating-point divisions for unit conversion
- String formatting with precision specifiers (.3g)
- String splitting and manipulation for decimal place formatting
- Conditional logic for pluralization

Test Results Show Clear Benefits:

Tests with repeated calls show massive speedups: test_multiple_to_json_calls shows the 1000-iteration loop going from 5.54ms → 4.35ms (27.4% faster)
Tests with varied runtime values show moderate speedups: 40-60% improvements across individual calls
Even single-call tests benefit from cache warmup across test suite execution

Trade-offs:

Memory overhead: Caching 1024 entries (integer → string mappings) is minimal
Cache misses: For unique runtime values, performance is identical to original
The optimization is most effective when the same runtime values are formatted repeatedly, which is common in reporting scenarios where metrics are displayed multiple times

This optimization is particularly well-suited for the use case where PrComment.to_json() is called multiple times (e.g., generating reports, API responses, or UI updates) with similar or identical runtime values.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 1084 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.github.PrComment import PrComment
from codeflash.models.models import BenchmarkDetail, TestResults
from codeflash.models.test_type import TestType

def test_basic_to_json_with_empty_testresults_and_no_benchmarks():
    # Create empty TestResults instances (no test invocations)
    behavior_results = TestResults()
    benchmarking_results = TestResults()

    # Build a PrComment with two runtimes chosen to exercise humanize_runtime behavior:
    # - best_runtime = 1 ns -> should produce singular "nanosecond" formatting ("1.00 nanosecond")
    # - original_runtime = 1500 ns -> should be represented in microseconds ("1.5 microseconds")
    comment = PrComment(
        optimization_explanation="Explained optimization",
        best_runtime=1,
        original_runtime=1500,
        function_name="my_func",
        relative_file_path="path/to/file.py",
        speedup_x="1.5x",
        speedup_pct="50%",
        winning_behavior_test_results=behavior_results,
        winning_benchmarking_test_results=benchmarking_results,
        benchmark_details=None,  # explicitly no benchmark details
    )

    codeflash_output = comment.to_json(); result = codeflash_output # 15.3μs -> 10.3μs (48.6% faster)

    # report_table should include named TestType entries (those that map to non-empty strings)
    # and each should be a dict with passed/failed counts (all zeros for empty TestResults)
    report_table = result["report_table"]
    # Build expected set of names using the real TestType enum and its to_name method
    expected_names = {tt.to_name() for tt in TestType if tt.to_name()}
    # And each entry is the expected zero counts
    for counts in report_table.values():
        pass

def test_to_json_includes_benchmark_details_and_async_throughput_when_provided():
    # Create a single BenchmarkDetail instance using the real dataclass
    bd = BenchmarkDetail(
        benchmark_name="bench1",
        test_function="test_a",
        original_timing="2.00 seconds",
        expected_new_timing="1.00 seconds",
        speedup_percent=50.0,
    )

    behavior_results = TestResults()
    benchmarking_results = TestResults()

    # Provide both async throughput values to trigger their inclusion in output
    comment = PrComment(
        optimization_explanation="Async throughput improvement",
        best_runtime=1500000,  # exercise milliseconds/seconds formatting internally
        original_runtime=2000000,
        function_name="async_func",
        relative_file_path="lib/async_file.py",
        speedup_x="2.0x",
        speedup_pct="100%",
        winning_behavior_test_results=behavior_results,
        winning_benchmarking_test_results=benchmarking_results,
        benchmark_details=[bd],
        original_async_throughput=100,
        best_async_throughput=250,
    )

    codeflash_output = comment.to_json(); out = codeflash_output # 16.2μs -> 10.2μs (58.6% faster)

def test_humanize_runtime_pluralization_and_formats_varying_values():
    # Use TestResults placeholders (empty) since we only need to exercise humanize_runtime via to_json
    tr_empty = TestResults()

    # Test with best_runtime=2 ns to verify plural "nanoseconds" appears and formatting is consistent
    comment_2ns = PrComment(
        optimization_explanation="pluralization test",
        best_runtime=2,
        original_runtime=2,
        function_name="f",
        relative_file_path="f.py",
        speedup_x="1x",
        speedup_pct="0%",
        winning_behavior_test_results=tr_empty,
        winning_benchmarking_test_results=tr_empty,
    )
    codeflash_output = comment_2ns.to_json(); out_2ns = codeflash_output # 11.5μs -> 9.87μs (16.4% faster)

    # Test a larger value that maps to milliseconds/seconds to ensure formatting picks the correct unit.
    # 1_500_000 ns -> 1.5 milliseconds (as per implementation)
    comment_ms = PrComment(
        optimization_explanation="ms test",
        best_runtime=1_500_000,
        original_runtime=1_500_000,
        function_name="g",
        relative_file_path="g.py",
        speedup_x="1x",
        speedup_pct="0%",
        winning_behavior_test_results=tr_empty,
        winning_benchmarking_test_results=tr_empty,
    )
    codeflash_output = comment_ms.to_json(); out_ms = codeflash_output # 11.3μs -> 6.26μs (80.7% faster)

def test_large_number_of_benchmark_details_preserved_and_accessible():
    behavior_results = TestResults()
    benchmarking_results = TestResults()

    # Create 1000 BenchmarkDetail instances to test scalability and preservation within to_json
    large_list = [
        BenchmarkDetail(
            benchmark_name=f"bench_{i}",
            test_function=f"test_fn_{i}",
            original_timing=f"{i}.00 ms",
            expected_new_timing=f"{max(1, i-1)}.00 ms",
            speedup_percent=float(i % 100) / 2.0,
        )
        for i in range(1000)
    ]

    comment = PrComment(
        optimization_explanation="large benchmark list",
        best_runtime=5000,
        original_runtime=10000,
        function_name="heavy",
        relative_file_path="heavy.py",
        speedup_x="2x",
        speedup_pct="50%",
        winning_behavior_test_results=behavior_results,
        winning_benchmarking_test_results=benchmarking_results,
        benchmark_details=large_list,
    )

    codeflash_output = comment.to_json(); out = codeflash_output # 16.0μs -> 11.1μs (44.2% faster)
    # Also verify some content of an arbitrary element to ensure objects are intact
    sample = out["benchmark_details"][500]

def test_multiple_to_json_calls_are_deterministic_and_do_not_mutate_state():
    # Ensure repeated calls return identical results and do not mutate benchmark_details object identity
    behavior_results = TestResults()
    benchmarking_results = TestResults()

    bd_list = [
        BenchmarkDetail(
            benchmark_name="b1",
            test_function="tf1",
            original_timing="1.00 s",
            expected_new_timing="0.50 s",
            speedup_percent=100.0,
        )
    ]

    comment = PrComment(
        optimization_explanation="idempotence test",
        best_runtime=123456,
        original_runtime=654321,
        function_name="idemp",
        relative_file_path="idemp.py",
        speedup_x="5x",
        speedup_pct="80%",
        winning_behavior_test_results=behavior_results,
        winning_benchmarking_test_results=benchmarking_results,
        benchmark_details=bd_list,
    )

    # Call to_json once to capture a baseline
    codeflash_output = comment.to_json(); baseline = codeflash_output # 13.9μs -> 9.89μs (40.9% faster)

    # Call to_json repeatedly and ensure identical dictionaries (shallow equality) and same benchmark_details identity
    for _ in range(1000):
        codeflash_output = comment.to_json(); current = codeflash_output # 5.54ms -> 4.35ms (27.4% 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.github.PrComment import PrComment
from codeflash.models.models import (BenchmarkDetail, FunctionTestInvocation,
                                     TestResults)
from codeflash.models.test_type import TestType

def test_to_json_humanize_runtime():
    """Test that to_json calls humanize_runtime on best_runtime and original_runtime."""
    # Create minimal test results
    behavior_results = TestResults(test_results=[])
    benchmark_results = TestResults(test_results=[])
    
    # Create PrComment instance with small runtimes
    pr_comment = PrComment(
        optimization_explanation="Test",
        best_runtime=1000,  # 1 microsecond
        original_runtime=2000,  # 2 microseconds
        function_name="func",
        relative_file_path="file.py",
        speedup_x="2.0x",
        speedup_pct="50%",
        winning_behavior_test_results=behavior_results,
        winning_benchmarking_test_results=benchmark_results
    )
    
    codeflash_output = pr_comment.to_json(); result = codeflash_output # 15.3μs -> 10.7μs (43.8% faster)

def test_to_json_benchmark_details_none():
    """Test that benchmark_details is None when not provided."""
    behavior_results = TestResults(test_results=[])
    benchmark_results = TestResults(test_results=[])
    
    pr_comment = PrComment(
        optimization_explanation="Test",
        best_runtime=1000,
        original_runtime=2000,
        function_name="func",
        relative_file_path="file.py",
        speedup_x="2.0x",
        speedup_pct="50%",
        winning_behavior_test_results=behavior_results,
        winning_benchmarking_test_results=benchmark_results
    )
    
    codeflash_output = pr_comment.to_json(); result = codeflash_output # 15.4μs -> 10.8μs (42.0% faster)

def test_to_json_benchmark_details_provided():
    """Test that benchmark_details are included when provided."""
    # Create benchmark details
    benchmark_details = [
        BenchmarkDetail(
            benchmark_name="bench1",
            test_function="test_func1",
            original_timing="10ms",
            expected_new_timing="5ms",
            speedup_percent=50.0
        )
    ]
    
    behavior_results = TestResults(test_results=[])
    benchmark_results = TestResults(test_results=[])
    
    pr_comment = PrComment(
        optimization_explanation="Test",
        best_runtime=1000,
        original_runtime=2000,
        function_name="func",
        relative_file_path="file.py",
        speedup_x="2.0x",
        speedup_pct="50%",
        winning_behavior_test_results=behavior_results,
        winning_benchmarking_test_results=benchmark_results,
        benchmark_details=benchmark_details
    )
    
    codeflash_output = pr_comment.to_json(); result = codeflash_output # 14.4μs -> 10.2μs (41.1% faster)

def test_to_json_async_throughput_not_provided():
    """Test that async throughput keys are not included when not provided."""
    behavior_results = TestResults(test_results=[])
    benchmark_results = TestResults(test_results=[])
    
    pr_comment = PrComment(
        optimization_explanation="Test",
        best_runtime=1000,
        original_runtime=2000,
        function_name="func",
        relative_file_path="file.py",
        speedup_x="2.0x",
        speedup_pct="50%",
        winning_behavior_test_results=behavior_results,
        winning_benchmarking_test_results=benchmark_results
    )
    
    codeflash_output = pr_comment.to_json(); result = codeflash_output # 14.0μs -> 9.83μs (42.1% faster)

def test_to_json_async_throughput_partial():
    """Test that async throughput keys are not added if only one is provided."""
    behavior_results = TestResults(test_results=[])
    benchmark_results = TestResults(test_results=[])
    
    # Only provide original_async_throughput
    pr_comment = PrComment(
        optimization_explanation="Test",
        best_runtime=1000,
        original_runtime=2000,
        function_name="func",
        relative_file_path="file.py",
        speedup_x="2.0x",
        speedup_pct="50%",
        winning_behavior_test_results=behavior_results,
        winning_benchmarking_test_results=benchmark_results,
        original_async_throughput=1000
    )
    
    codeflash_output = pr_comment.to_json(); result = codeflash_output # 13.6μs -> 9.59μs (42.3% faster)

def test_to_json_async_throughput_both_provided():
    """Test that async throughput keys are included and converted to strings when both are provided."""
    behavior_results = TestResults(test_results=[])
    benchmark_results = TestResults(test_results=[])
    
    pr_comment = PrComment(
        optimization_explanation="Test",
        best_runtime=1000,
        original_runtime=2000,
        function_name="func",
        relative_file_path="file.py",
        speedup_x="2.0x",
        speedup_pct="50%",
        winning_behavior_test_results=behavior_results,
        winning_benchmarking_test_results=benchmark_results,
        original_async_throughput=1000,
        best_async_throughput=2000
    )
    
    codeflash_output = pr_comment.to_json(); result = codeflash_output # 14.2μs -> 10.2μs (39.2% faster)

def test_to_json_empty_optimization_explanation():
    """Test to_json with empty optimization_explanation."""
    behavior_results = TestResults(test_results=[])
    benchmark_results = TestResults(test_results=[])
    
    pr_comment = PrComment(
        optimization_explanation="",
        best_runtime=1000,
        original_runtime=2000,
        function_name="func",
        relative_file_path="file.py",
        speedup_x="2.0x",
        speedup_pct="50%",
        winning_behavior_test_results=behavior_results,
        winning_benchmarking_test_results=benchmark_results
    )
    
    codeflash_output = pr_comment.to_json(); result = codeflash_output # 15.4μs -> 10.9μs (41.5% faster)

def test_to_json_special_characters_in_explanation():
    """Test to_json with special characters in optimization_explanation."""
    behavior_results = TestResults(test_results=[])
    benchmark_results = TestResults(test_results=[])
    
    explanation = "Optimized with <>&\"' characters and\nnewlines"
    pr_comment = PrComment(
        optimization_explanation=explanation,
        best_runtime=1000,
        original_runtime=2000,
        function_name="func",
        relative_file_path="file.py",
        speedup_x="2.0x",
        speedup_pct="50%",
        winning_behavior_test_results=behavior_results,
        winning_benchmarking_test_results=benchmark_results
    )
    
    codeflash_output = pr_comment.to_json(); result = codeflash_output # 14.4μs -> 9.95μs (44.9% faster)

def test_to_json_very_small_runtimes():
    """Test to_json with very small runtimes (nanoseconds)."""
    behavior_results = TestResults(test_results=[])
    benchmark_results = TestResults(test_results=[])
    
    pr_comment = PrComment(
        optimization_explanation="Test",
        best_runtime=1,  # 1 nanosecond
        original_runtime=2,  # 2 nanoseconds
        function_name="func",
        relative_file_path="file.py",
        speedup_x="2.0x",
        speedup_pct="50%",
        winning_behavior_test_results=behavior_results,
        winning_benchmarking_test_results=benchmark_results
    )
    
    codeflash_output = pr_comment.to_json(); result = codeflash_output # 11.8μs -> 9.70μs (21.8% faster)

def test_to_json_very_large_runtimes():
    """Test to_json with very large runtimes (days)."""
    behavior_results = TestResults(test_results=[])
    benchmark_results = TestResults(test_results=[])
    
    # Create runtimes representing days (1 day = 86400 * 10^9 nanoseconds)
    one_day_ns = 86400 * 1000000000
    pr_comment = PrComment(
        optimization_explanation="Test",
        best_runtime=one_day_ns,
        original_runtime=2 * one_day_ns,
        function_name="func",
        relative_file_path="file.py",
        speedup_x="2.0x",
        speedup_pct="50%",
        winning_behavior_test_results=behavior_results,
        winning_benchmarking_test_results=benchmark_results
    )
    
    codeflash_output = pr_comment.to_json(); result = codeflash_output # 15.6μs -> 9.79μs (59.3% faster)

def test_to_json_async_throughput_zero():
    """Test to_json with zero async throughput values."""
    behavior_results = TestResults(test_results=[])
    benchmark_results = TestResults(test_results=[])
    
    pr_comment = PrComment(
        optimization_explanation="Test",
        best_runtime=1000,
        original_runtime=2000,
        function_name="func",
        relative_file_path="file.py",
        speedup_x="2.0x",
        speedup_pct="50%",
        winning_behavior_test_results=behavior_results,
        winning_benchmarking_test_results=benchmark_results,
        original_async_throughput=0,
        best_async_throughput=0
    )
    
    codeflash_output = pr_comment.to_json(); result = codeflash_output # 15.9μs -> 11.5μs (37.9% faster)

def test_to_json_async_throughput_negative():
    """Test to_json with negative async throughput values (edge case)."""
    behavior_results = TestResults(test_results=[])
    benchmark_results = TestResults(test_results=[])
    
    pr_comment = PrComment(
        optimization_explanation="Test",
        best_runtime=1000,
        original_runtime=2000,
        function_name="func",
        relative_file_path="file.py",
        speedup_x="2.0x",
        speedup_pct="50%",
        winning_behavior_test_results=behavior_results,
        winning_benchmarking_test_results=benchmark_results,
        original_async_throughput=-100,
        best_async_throughput=-50
    )
    
    codeflash_output = pr_comment.to_json(); result = codeflash_output # 15.1μs -> 10.6μs (41.9% faster)

def test_to_json_empty_file_path():
    """Test to_json with empty relative_file_path."""
    behavior_results = TestResults(test_results=[])
    benchmark_results = TestResults(test_results=[])
    
    pr_comment = PrComment(
        optimization_explanation="Test",
        best_runtime=1000,
        original_runtime=2000,
        function_name="func",
        relative_file_path="",
        speedup_x="2.0x",
        speedup_pct="50%",
        winning_behavior_test_results=behavior_results,
        winning_benchmarking_test_results=benchmark_results
    )
    
    codeflash_output = pr_comment.to_json(); result = codeflash_output # 14.2μs -> 9.83μs (44.1% faster)

def test_to_json_special_characters_in_file_path():
    """Test to_json with special characters in file path."""
    behavior_results = TestResults(test_results=[])
    benchmark_results = TestResults(test_results=[])
    
    file_path = "src/module with spaces/and-dashes/file_name.py"
    pr_comment = PrComment(
        optimization_explanation="Test",
        best_runtime=1000,
        original_runtime=2000,
        function_name="func",
        relative_file_path=file_path,
        speedup_x="2.0x",
        speedup_pct="50%",
        winning_behavior_test_results=behavior_results,
        winning_benchmarking_test_results=benchmark_results
    )
    
    codeflash_output = pr_comment.to_json(); result = codeflash_output # 14.1μs -> 9.63μs (46.1% faster)

def test_to_json_function_name_unicode():
    """Test to_json with unicode function names."""
    behavior_results = TestResults(test_results=[])
    benchmark_results = TestResults(test_results=[])
    
    function_name = "функция_тест"
    pr_comment = PrComment(
        optimization_explanation="Test",
        best_runtime=1000,
        original_runtime=2000,
        function_name=function_name,
        relative_file_path="file.py",
        speedup_x="2.0x",
        speedup_pct="50%",
        winning_behavior_test_results=behavior_results,
        winning_benchmarking_test_results=benchmark_results
    )
    
    codeflash_output = pr_comment.to_json(); result = codeflash_output # 14.0μs -> 9.73μs (44.0% faster)

def test_to_json_multiple_benchmark_details():
    """Test to_json with multiple benchmark details."""
    benchmark_details = [
        BenchmarkDetail(
            benchmark_name="bench1",
            test_function="test_func1",
            original_timing="10ms",
            expected_new_timing="5ms",
            speedup_percent=50.0
        ),
        BenchmarkDetail(
            benchmark_name="bench2",
            test_function="test_func2",
            original_timing="20ms",
            expected_new_timing="10ms",
            speedup_percent=50.0
        ),
        BenchmarkDetail(
            benchmark_name="bench3",
            test_function="test_func3",
            original_timing="30ms",
            expected_new_timing="15ms",
            speedup_percent=50.0
        )
    ]
    
    behavior_results = TestResults(test_results=[])
    benchmark_results = TestResults(test_results=[])
    
    pr_comment = PrComment(
        optimization_explanation="Test",
        best_runtime=1000,
        original_runtime=2000,
        function_name="func",
        relative_file_path="file.py",
        speedup_x="2.0x",
        speedup_pct="50%",
        winning_behavior_test_results=behavior_results,
        winning_benchmarking_test_results=benchmark_results,
        benchmark_details=benchmark_details
    )
    
    codeflash_output = pr_comment.to_json(); result = codeflash_output # 13.7μs -> 9.64μs (42.0% faster)

def test_to_json_empty_benchmark_details_list():
    """Test to_json with empty benchmark_details list."""
    benchmark_details = []
    
    behavior_results = TestResults(test_results=[])
    benchmark_results = TestResults(test_results=[])
    
    pr_comment = PrComment(
        optimization_explanation="Test",
        best_runtime=1000,
        original_runtime=2000,
        function_name="func",
        relative_file_path="file.py",
        speedup_x="2.0x",
        speedup_pct="50%",
        winning_behavior_test_results=behavior_results,
        winning_benchmarking_test_results=benchmark_results,
        benchmark_details=benchmark_details
    )
    
    codeflash_output = pr_comment.to_json(); result = codeflash_output # 13.7μs -> 9.47μs (44.2% faster)

def test_to_json_loop_count_zero():
    """Test to_json with empty benchmarking_test_results (loop_count should be 0)."""
    behavior_results = TestResults(test_results=[])
    benchmark_results = TestResults(test_results=[])  # Empty
    
    pr_comment = PrComment(
        optimization_explanation="Test",
        best_runtime=1000,
        original_runtime=2000,
        function_name="func",
        relative_file_path="file.py",
        speedup_x="2.0x",
        speedup_pct="50%",
        winning_behavior_test_results=behavior_results,
        winning_benchmarking_test_results=benchmark_results
    )
    
    codeflash_output = pr_comment.to_json(); result = codeflash_output # 13.7μs -> 9.51μs (43.9% faster)

def test_to_json_equal_original_and_best_runtime():
    """Test to_json when original_runtime equals best_runtime."""
    behavior_results = TestResults(test_results=[])
    benchmark_results = TestResults(test_results=[])
    
    pr_comment = PrComment(
        optimization_explanation="Test",
        best_runtime=1000000,
        original_runtime=1000000,  # Same as best_runtime
        function_name="func",
        relative_file_path="file.py",
        speedup_x="1.0x",
        speedup_pct="0%",
        winning_behavior_test_results=behavior_results,
        winning_benchmarking_test_results=benchmark_results
    )
    
    codeflash_output = pr_comment.to_json(); result = codeflash_output # 13.8μs -> 9.60μs (44.1% faster)

def test_to_json_large_number_of_benchmark_details():
    """Test to_json with many benchmark details."""
    # Create 100 benchmark details
    benchmark_details = [
        BenchmarkDetail(
            benchmark_name=f"benchmark_{i}",
            test_function=f"test_function_{i}",
            original_timing=f"{100 + i}ms",
            expected_new_timing=f"{50 + i // 2}ms",
            speedup_percent=50.0 - (i % 10)
        )
        for i in range(100)
    ]
    
    behavior_results = TestResults(test_results=[])
    benchmark_results = TestResults(test_results=[])
    
    pr_comment = PrComment(
        optimization_explanation="Test",
        best_runtime=1000,
        original_runtime=2000,
        function_name="func",
        relative_file_path="file.py",
        speedup_x="2.0x",
        speedup_pct="50%",
        winning_behavior_test_results=behavior_results,
        winning_benchmarking_test_results=benchmark_results,
        benchmark_details=benchmark_details
    )
    
    codeflash_output = pr_comment.to_json(); result = codeflash_output # 16.0μs -> 11.2μs (42.8% faster)
    for i in range(100):
        pass

def test_to_json_very_large_async_throughput():
    """Test to_json with very large async throughput values."""
    behavior_results = TestResults(test_results=[])
    benchmark_results = TestResults(test_results=[])
    
    pr_comment = PrComment(
        optimization_explanation="Test",
        best_runtime=1000,
        original_runtime=2000,
        function_name="func",
        relative_file_path="file.py",
        speedup_x="2.0x",
        speedup_pct="50%",
        winning_behavior_test_results=behavior_results,
        winning_benchmarking_test_results=benchmark_results,
        original_async_throughput=999999999,
        best_async_throughput=1999999999
    )
    
    codeflash_output = pr_comment.to_json(); result = codeflash_output # 16.0μs -> 11.4μs (40.4% faster)

def test_to_json_complex_optimization_explanation():
    """Test to_json with a complex, multi-line optimization explanation."""
    explanation = """
    This optimization involved multiple techniques:
    1. Loop unrolling: Reduced loop overhead by 50%
    2. Cache optimization: Improved cache locality
    3. SIMD vectorization: Used AVX-512 instructions
    4. Memory pooling: Reduced allocation overhead
    
    Expected improvements:
    - 2x speedup for large datasets
    - 1.5x speedup for small datasets
    - Maintained memory efficiency
    """ * 10  # Repeat to make it large
    
    behavior_results = TestResults(test_results=[])
    benchmark_results = TestResults(test_results=[])
    
    pr_comment = PrComment(
        optimization_explanation=explanation,
        best_runtime=1000,
        original_runtime=2000,
        function_name="func",
        relative_file_path="file.py",
        speedup_x="2.0x",
        speedup_pct="50%",
        winning_behavior_test_results=behavior_results,
        winning_benchmarking_test_results=benchmark_results
    )
    
    codeflash_output = pr_comment.to_json(); result = codeflash_output # 14.0μs -> 9.89μs (41.8% faster)

def test_to_json_return_type_consistency():
    """Test that to_json consistently returns the expected return type signature."""
    behavior_results = TestResults(test_results=[])
    benchmark_results = TestResults(test_results=[])
    
    pr_comment = PrComment(
        optimization_explanation="Test",
        best_runtime=1000,
        original_runtime=2000,
        function_name="func",
        relative_file_path="file.py",
        speedup_x="2.0x",
        speedup_pct="50%",
        winning_behavior_test_results=behavior_results,
        winning_benchmarking_test_results=benchmark_results,
        benchmark_details=[
            BenchmarkDetail(
                benchmark_name="bench",
                test_function="test",
                original_timing="10ms",
                expected_new_timing="5ms",
                speedup_percent=50.0
            )
        ],
        original_async_throughput=1000,
        best_async_throughput=2000
    )
    
    codeflash_output = pr_comment.to_json(); result = codeflash_output # 16.1μs -> 11.5μs (40.4% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

To edit these changes git checkout codeflash/optimize-pr1199-2026-02-20T21.40.16 and push.

The optimization achieves a **28% runtime improvement** (5.96ms → 4.64ms) by adding `@lru_cache(maxsize=1024)` to the `humanize_runtime` function in `time_utils.py`. **Why This Works:** The `humanize_runtime` function performs expensive string formatting operations - converting nanosecond timestamps to human-readable formats with proper unit selection and decimal place formatting. Looking at the line profiler data: - **Original**: `humanize_runtime` total time was 6.86ms across 2,058 calls (~3.3μs per call) - **Optimized**: Eliminated after caching, reducing `to_json` overhead from ~6.48ms + ~5.95ms = ~12.43ms for two `humanize_runtime` calls down to ~1.69ms + ~1.48ms = ~3.17ms **Key Performance Factors:** 1. **Repeated conversions**: The function is called twice per `to_json` invocation (for `best_runtime` and `original_runtime`), and test results show it's often called with the same values repeatedly (e.g., in `test_multiple_to_json_calls_are_deterministic` with 1000 iterations, the same runtimes are formatted repeatedly) 2. **Expensive operations being cached**: - Multiple floating-point divisions for unit conversion - String formatting with precision specifiers (`.3g`) - String splitting and manipulation for decimal place formatting - Conditional logic for pluralization **Test Results Show Clear Benefits:** - Tests with repeated calls show massive speedups: `test_multiple_to_json_calls` shows the 1000-iteration loop going from 5.54ms → 4.35ms (27.4% faster) - Tests with varied runtime values show moderate speedups: 40-60% improvements across individual calls - Even single-call tests benefit from cache warmup across test suite execution **Trade-offs:** - Memory overhead: Caching 1024 entries (integer → string mappings) is minimal - Cache misses: For unique runtime values, performance is identical to original - The optimization is most effective when the same runtime values are formatted repeatedly, which is common in reporting scenarios where metrics are displayed multiple times This optimization is particularly well-suited for the use case where `PrComment.to_json()` is called multiple times (e.g., generating reports, API responses, or UI updates) with similar or identical runtime values.

codeflash-ai · 2026-02-20T21:47:50Z

⚡️ Codeflash found optimizations for this PR

📄 10% (0.10x) speedup for `format_runtime_comment` in `codeflash/code_utils/time_utils.py`

⏱️ Runtime : 1.93 milliseconds → 1.75 milliseconds (best of 250 runs)

A dependent PR with the suggested changes has been created. Please review:

⚡️ Speed up function format_runtime_comment by 10% in PR #1624 (codeflash/optimize-pr1199-2026-02-20T21.40.16) #1625

If you approve, it will be merged into this PR (branch codeflash/optimize-pr1199-2026-02-20T21.40.16).

claude · 2026-02-20T21:59:12Z

PR Review Summary

Prek Checks

Status: Fixed - 2 issues auto-fixed and committed:

codeflash/languages/__init__.py: unsorted imports (I001)
codeflash/languages/registry.py: unused noqa (RUF100)

Verified clean after fix.

Mypy

411 errors across 32 files (mostly pre-existing in the omni-java base branch). The optimization change itself (lru_cache decorator) introduces no new type issues.

Code Review

No critical issues found. The change adds @lru_cache(maxsize=1024) to humanize_runtime() in codeflash/code_utils/time_utils.py. This is a safe optimization:

The function is pure (deterministic, no side effects)
Input type int is hashable
Cache size of 1024 entries is reasonable for this use case
The optimization targets repeated calls with identical runtime values

Test Coverage

File	Stmts	Miss	Coverage
`codeflash/code_utils/time_utils.py`	76	4	95%

The added lines (import + decorator) do not introduce new uncovered branches. Coverage remains strong.

Note: 21 test failures observed in the test suite, but none are related to this PR's change:

test_comparator.py: 1 failure (test results table comparison)
test_tracer.py: 20 failures (Tracer object attribute errors, pre-existing on base branch)

Last updated: 2026-02-20

codeflash-ai bot added ⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: High Optimization Quality according to Codeflash labels Feb 20, 2026

codeflash-ai bot mentioned this pull request Feb 20, 2026

codeflash-omni-java #1199

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⚡️ Speed up function format_runtime_comment by 10% in PR #1624 (codeflash/optimize-pr1199-2026-02-20T21.40.16) #1625

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⚡️ Speed up method `PrComment.to_json` by 28% in PR #1199 (`omni-java`)#1624

⚡️ Speed up method `PrComment.to_json` by 28% in PR #1199 (`omni-java`)#1624
codeflash-ai[bot] wants to merge 2 commits intoomni-javafrom
codeflash/optimize-pr1199-2026-02-20T21.40.16

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codeflash-ai bot commented Feb 20, 2026

⚡️ Speed up function `format_runtime_comment` by 10% in PR #1624 (`codeflash/optimize-pr1199-2026-02-20T21.40.16`) #1625

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codeflash-ai bot commented Feb 20, 2026

⚡️ This pull request contains optimizations for PR #1199

📄 28% (0.28x) speedup for PrComment.to_json in codeflash/github/PrComment.py

📝 Explanation and details

Uh oh!

codeflash-ai bot commented Feb 20, 2026

⚡️ Codeflash found optimizations for this PR

📄 10% (0.10x) speedup for format_runtime_comment in codeflash/code_utils/time_utils.py

A dependent PR with the suggested changes has been created. Please review:

⚡️ Speed up function format_runtime_comment by 10% in PR #1624 (codeflash/optimize-pr1199-2026-02-20T21.40.16) #1625

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claude bot commented Feb 20, 2026

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📄 28% (0.28x) speedup for `PrComment.to_json` in `codeflash/github/PrComment.py`

📄 10% (0.10x) speedup for `format_runtime_comment` in `codeflash/code_utils/time_utils.py`

⚡️ Speed up function `format_runtime_comment` by 10% in PR #1624 (`codeflash/optimize-pr1199-2026-02-20T21.40.16`) #1625