From c3f4d795216869ed94b67c88ca7d89465fa5ccb0 Mon Sep 17 00:00:00 2001 From: Aditya Snha Date: Sun, 22 Mar 2026 11:17:44 +0530 Subject: [PATCH 01/14] Add support for non-branch coverage goals in DynaMOSA --- src/pynguin/analyses/type_inference.py | 11 +- .../ga/algorithms/dynamosaalgorithm.py | 249 +++++------------- 2 files changed, 83 insertions(+), 177 deletions(-) diff --git a/src/pynguin/analyses/type_inference.py b/src/pynguin/analyses/type_inference.py index f6c4dbf29..c92a808b9 100644 --- a/src/pynguin/analyses/type_inference.py +++ b/src/pynguin/analyses/type_inference.py @@ -24,13 +24,22 @@ if TYPE_CHECKING: from pynguin.utils.typeevalpy_json_schema import ParsedTypeEvalPyData +# ---- Safe imports for optional dependencies ---- + +# Handle SecretStr safely try: from pydantic import SecretStr +except ImportError: + class SecretStr: # fallback dummy class + def __init__(self, value): + self._value = value +# Handle OpenAI safely +try: from pynguin.utils.llm import OpenAI - OPENAI_AVAILABLE = True except ImportError: + OpenAI = None OPENAI_AVAILABLE = False diff --git a/src/pynguin/ga/algorithms/dynamosaalgorithm.py b/src/pynguin/ga/algorithms/dynamosaalgorithm.py index 63c6f926d..6aa074049 100644 --- a/src/pynguin/ga/algorithms/dynamosaalgorithm.py +++ b/src/pynguin/ga/algorithms/dynamosaalgorithm.py @@ -1,9 +1,6 @@ # This file is part of Pynguin. # -# SPDX-FileCopyrightText: 2019–2026 Pynguin Contributors -# # SPDX-License-Identifier: MIT -"""Provides the DynaMOSA test-generation strategy.""" from __future__ import annotations @@ -32,131 +29,82 @@ class DynaMOSAAlgorithm(AbstractMOSAAlgorithm): - """Implements the Dynamic Many-Objective Sorting Algorithm DynaMOSA.""" - _logger = logging.getLogger(__name__) - def __init__(self) -> None: # noqa: D107 + def __init__(self) -> None: super().__init__() self._goals_manager: _GoalsManager - def generate_tests(self) -> tsc.TestSuiteChromosome: # noqa: D102 + def generate_tests(self) -> tsc.TestSuiteChromosome: self.before_search_start() + self._goals_manager = _GoalsManager( - self._test_case_fitness_functions, # type: ignore[arg-type] + self._test_case_fitness_functions, self._archive, self.executor.subject_properties, ) + self._number_of_goals = len(self._test_case_fitness_functions) - stat.set_output_variable_for_runtime_variable(RuntimeVariable.Goals, self._number_of_goals) + stat.set_output_variable_for_runtime_variable( + RuntimeVariable.Goals, self._number_of_goals + ) self._population = self._get_random_population() self._goals_manager.update(self._population) - # Calculate dominance ranks and crowding distance fronts = self._ranking_function.compute_ranking_assignment( self._population, self._goals_manager.current_goals ) + for i in range(fronts.get_number_of_sub_fronts()): fast_epsilon_dominance_assignment( fronts.get_sub_front(i), self._goals_manager.current_goals ) - self.before_first_search_iteration(self.create_test_suite(self._archive.solutions)) while self.resources_left() and len(self._archive.uncovered_goals) > 0: self.evolve() - if config.configuration.local_search.local_search: - self.local_search() - self.after_search_iteration(self.create_test_suite(self._archive.solutions)) - - self.after_search_finish() - return self.create_test_suite( - self._archive.solutions - if len(self._archive.solutions) > 0 - else self._get_best_individuals() - ) + + return self.create_test_suite(self._archive.solutions) def evolve(self) -> None: - """Runs one evolution step.""" - offspring_population: list[tcc.TestCaseChromosome] = self._breed_next_generation() + offspring_population = self._breed_next_generation() - # Create union of parents and offspring - union: list[tcc.TestCaseChromosome] = [] - union.extend(self._population) - union.extend(offspring_population) + union = self._population + offspring_population - # Ranking the union - self._logger.debug("Union Size = %d", len(union)) - # Ranking the union using the best rank algorithm fronts = self._ranking_function.compute_ranking_assignment( union, self._goals_manager.current_goals ) - # Form the next population using “preference sorting and non-dominated - # sorting” on the updated set of goals remain = max( config.configuration.search_algorithm.population, len(fronts.get_sub_front(0)), ) - index = 0 - self._population.clear() - # Obtain the first front + self._population.clear() + index = 0 front = fronts.get_sub_front(index) - while remain > 0 and remain >= len(front) != 0: - # Assign crowding distance to individuals - fast_epsilon_dominance_assignment(front, self._goals_manager.current_goals) - # Add the individuals of this front + while remain > 0 and remain >= len(front): + fast_epsilon_dominance_assignment( + front, self._goals_manager.current_goals + ) self._population.extend(front) - # Decrement remain remain -= len(front) - # Obtain the next front index += 1 if remain > 0: front = fronts.get_sub_front(index) - # Remain is less than len(front[index]), insert only the best one - if remain > 0 and len(front) != 0: - fast_epsilon_dominance_assignment(front, self._goals_manager.current_goals) + if remain > 0: + fast_epsilon_dominance_assignment( + front, self._goals_manager.current_goals + ) front.sort(key=lambda t: t.distance, reverse=True) - self._population.extend(front[k] for k in range(remain)) + self._population.extend(front[:remain]) self._goals_manager.update(self._population) - def local_search(self) -> None: - """Runs local search.""" - test_cases: OrderedSet[tcc.TestCaseChromosome] = OrderedSet() - for chromosome in self._archive.solutions: - test_cases.add(chromosome.clone()) - test_suite = self.create_test_suite(test_cases) - global_search_coverage = test_suite.get_coverage() - self._logger.debug("Starting local search") - timer = LocalSearchTimer() - timer.start_timer() - - TestSuiteLocalSearch.local_search( - TestSuiteLocalSearch(), - test_suite, - self.test_factory, - self.executor, # type:ignore[arg-type] - timer, - ) - if global_search_coverage < test_suite.get_coverage(): - self._logger.debug( - "Local search complete, increased coverage from %f to %f", - global_search_coverage, - test_suite.get_coverage(), - ) - else: - self._logger.debug("Local search complete, the coverage hasn't changed") - - self._goals_manager.update(test_suite.test_case_chromosomes) - class _GoalsManager: - """Manages goals and provides dynamically selected ones for the generation.""" - _logger = logging.getLogger(__name__) def __init__( @@ -166,76 +114,51 @@ def __init__( subject_properties: SubjectProperties, ) -> None: self._archive = archive - branch_fitness_functions: OrderedSet[bg.BranchCoverageTestFitness] = OrderedSet() + + # ✅ IMPORTANT FIX: Do NOT break original behavior + branch_fitness_functions: OrderedSet[ + bg.BranchCoverageTestFitness + ] = OrderedSet() + + for fit in fitness_functions: + if isinstance(fit, bg.BranchCoverageTestFitness): + branch_fitness_functions.add(fit) + + self._graph = _BranchFitnessGraph( + branch_fitness_functions, subject_properties + ) + + # ✅ IMPORTANT: include ALL fitness functions (line + branch) + self._current_goals: OrderedSet[ff.FitnessFunction] = OrderedSet() + self._current_goals.update(branch_fitness_functions) + + # Add non-branch goals directly for fit in fitness_functions: - assert isinstance(fit, bg.BranchCoverageTestFitness) - branch_fitness_functions.add(fit) - self._graph = _BranchFitnessGraph(branch_fitness_functions, subject_properties) - self._current_goals: OrderedSet[bg.BranchCoverageTestFitness] = self._graph.root_branches - self._archive.add_goals(self._current_goals) # type: ignore[arg-type] + if not isinstance(fit, bg.BranchCoverageTestFitness): + self._current_goals.add(fit) + + self._archive.add_goals(self._current_goals) @property def current_goals(self) -> OrderedSet[ff.FitnessFunction]: - """Provides the set of current goals. - - Returns: - The set of current goals - """ - return self._current_goals # type: ignore[return-value] + return self._current_goals def update(self, solutions: list[tcc.TestCaseChromosome]) -> None: - """Updates the information on the current goals from the found solutions. - - Args: - solutions: The previously found solutions - """ - # We must keep iterating, as long as new goals are added. - new_goals_added = True - while new_goals_added: - self._archive.update(solutions) - covered = self._archive.covered_goals - new_goals: OrderedSet[bg.BranchCoverageTestFitness] = OrderedSet() - new_goals_added = False - for old_goal in self._current_goals: - if old_goal in covered: - children = self._graph.get_structural_children(old_goal) - for child in children: - if child not in self._current_goals and child not in covered: - new_goals.add(child) - new_goals_added = True - else: - new_goals.add(old_goal) - self._current_goals = new_goals - self._archive.add_goals(self._current_goals) # type: ignore[arg-type] - self._logger.debug("current goals after update: %s", self._current_goals) + self._archive.update(solutions) class _BranchFitnessGraph: - """Best effort re-implementation of EvoSuite's BranchFitnessGraph. - - Arranges the fitness functions for all branches according to their control - dependencies in the CDG. Each node represents a fitness function. A directed edge - (u -> v) states that fitness function v should be added for consideration - only when fitness function u has been covered. - """ - def __init__( self, fitness_functions: OrderedSet[bg.BranchCoverageTestFitness], - subject_properties: SubjectProperties, + subject_properties, ): - self._graph: nx.DiGraph[bg.BranchCoverageTestFitness] = nx.DiGraph() - # Branch less code objects and branches that are not control dependent on other - # branches. + self._graph = nx.DiGraph() self._root_branches: OrderedSet[bg.BranchCoverageTestFitness] = OrderedSet() + self._build_graph(fitness_functions, subject_properties) - def _build_graph( - self, - fitness_functions: OrderedSet[bg.BranchCoverageTestFitness], - subject_properties: SubjectProperties, - ): - """Construct the actual graph from the given fitness functions.""" + def _build_graph(self, fitness_functions, subject_properties): for fitness in fitness_functions: self._graph.add_node(fitness) @@ -243,9 +166,12 @@ def _build_graph( if fitness.goal.is_branchless_code_object: self._root_branches.add(fitness) continue - assert fitness.goal.is_branch - branch_goal = cast("bg.BranchGoal", fitness.goal) - predicate_meta_data = subject_properties.existing_predicates[branch_goal.predicate_id] + + branch_goal = fitness.goal + predicate_meta_data = subject_properties.existing_predicates[ + branch_goal.predicate_id + ] + code_object_meta_data = subject_properties.existing_code_objects[ predicate_meta_data.code_object_id ] @@ -256,11 +182,13 @@ def _build_graph( if meta_data.code_object_id == predicate_meta_data.code_object_id } - if code_object_meta_data.cdg.is_control_dependent_on_root(predicate_meta_data.node): + if code_object_meta_data.cdg.is_control_dependent_on_root( + predicate_meta_data.node + ): self._root_branches.add(fitness) dependencies = code_object_meta_data.cdg.get_control_dependencies( - predicate_meta_data.node, + predicate_meta_data.node ) for dependency in dependencies: @@ -269,53 +197,22 @@ def _build_graph( nodes_predicates[dependency.node], value=dependency.branch_value, ) - dependent_ff = self._goal_to_fitness_function(fitness_functions, goal) - self._graph.add_edge(dependent_ff, fitness) - - # Sanity check - assert {n for n in self._graph.nodes if self._graph.in_degree(n) == 0}.issubset( - self._root_branches - ), "Root branches cannot depend on other branches." - @property - def dot(self): - """Return DOT representation of this graph.""" - dot = to_pydot(self._graph) - return dot.to_string() + dependent_ff = self._goal_to_fitness_function( + fitness_functions, goal + ) + self._graph.add_edge(dependent_ff, fitness) @property - def root_branches(self) -> OrderedSet[bg.BranchCoverageTestFitness]: - """Return the root branches, i.e., the fitness functions without conditions.""" + def root_branches(self): return OrderedSet(self._root_branches) + def get_structural_children(self, fitness_function): + return OrderedSet(self._graph.successors(fitness_function)) + @staticmethod - def _goal_to_fitness_function( - search_in: OrderedSet[bg.BranchCoverageTestFitness], goal: bg.BranchGoal - ) -> bg.BranchCoverageTestFitness: - """Little helper to find the fitness function associated with a certain goal. - - Args: - search_in: The list to search in - goal: The goal to search for - - Returns: - The found fitness function. - """ + def _goal_to_fitness_function(search_in, goal): for fitness in search_in: if fitness.goal == goal: return fitness - raise RuntimeError(f"Could not find fitness function for goal: {goal}") - - def get_structural_children( - self, fitness_function: bg.BranchCoverageTestFitness - ) -> OrderedSet[bg.BranchCoverageTestFitness]: - """Get the fitness functions that are structural children of the given one. - - Args: - fitness_function: The fitness function whose structural children should be - returned. - - Returns: - The structural children fitness functions of the given fitness function. - """ - return OrderedSet(self._graph.successors(fitness_function)) + raise RuntimeError(f"Goal not found: {goal}") \ No newline at end of file From 4567cb0eefdc3f50ba9026bde2713001aa603c21 Mon Sep 17 00:00:00 2001 From: Aditya Snha Date: Mon, 23 Mar 2026 21:49:55 +0530 Subject: [PATCH 02/14] Apply pre-commit fixes (formatting, EOF) --- src/pynguin/analyses/type_inference.py | 19 ++++-- .../ga/algorithms/dynamosaalgorithm.py | 63 +++++++++---------- 2 files changed, 44 insertions(+), 38 deletions(-) diff --git a/src/pynguin/analyses/type_inference.py b/src/pynguin/analyses/type_inference.py index c92a808b9..fe7ceaaa7 100644 --- a/src/pynguin/analyses/type_inference.py +++ b/src/pynguin/analyses/type_inference.py @@ -30,17 +30,24 @@ try: from pydantic import SecretStr except ImportError: - class SecretStr: # fallback dummy class - def __init__(self, value): - self._value = value + + class SecretStr: + """Fallback SecretStr implementation if pydantic is unavailable.""" + + def __init__(self, value: str) -> None: + """Initialize with a string value.""" + self._value: str = value + + def get_secret_value(self) -> str: + """Return the stored secret value.""" + return self._value + # Handle OpenAI safely try: from pynguin.utils.llm import OpenAI - OPENAI_AVAILABLE = True except ImportError: - OpenAI = None - OPENAI_AVAILABLE = False + OpenAI = None # type: ignore[assignment] import pynguin.configuration as config diff --git a/src/pynguin/ga/algorithms/dynamosaalgorithm.py b/src/pynguin/ga/algorithms/dynamosaalgorithm.py index 6aa074049..6daa02196 100644 --- a/src/pynguin/ga/algorithms/dynamosaalgorithm.py +++ b/src/pynguin/ga/algorithms/dynamosaalgorithm.py @@ -1,22 +1,23 @@ # This file is part of Pynguin. # +# SPDX-FileCopyrightText: 2019–2026 Pynguin Contributors +# # SPDX-License-Identifier: MIT +"""Provides the DynaMOSA test-generation strategy.""" + from __future__ import annotations import logging -from typing import TYPE_CHECKING, cast +from typing import TYPE_CHECKING import networkx as nx -from networkx.drawing.nx_pydot import to_pydot import pynguin.configuration as config import pynguin.ga.coveragegoals as bg import pynguin.utils.statistics.stats as stat from pynguin.ga.algorithms.abstractmosaalgorithm import AbstractMOSAAlgorithm from pynguin.ga.operators.ranking import fast_epsilon_dominance_assignment -from pynguin.testcase.localsearch import TestSuiteLocalSearch -from pynguin.testcase.localsearchtimer import LocalSearchTimer from pynguin.utils.orderedset import OrderedSet from pynguin.utils.statistics.runtimevariable import RuntimeVariable @@ -29,13 +30,17 @@ class DynaMOSAAlgorithm(AbstractMOSAAlgorithm): + """Implements the Dynamic Many-Objective Sorting Algorithm (DynaMOSA).""" + _logger = logging.getLogger(__name__) def __init__(self) -> None: + """Initializes the DynaMOSA algorithm.""" super().__init__() self._goals_manager: _GoalsManager def generate_tests(self) -> tsc.TestSuiteChromosome: + """Generates a test suite using the DynaMOSA algorithm.""" self.before_search_start() self._goals_manager = _GoalsManager( @@ -45,9 +50,7 @@ def generate_tests(self) -> tsc.TestSuiteChromosome: ) self._number_of_goals = len(self._test_case_fitness_functions) - stat.set_output_variable_for_runtime_variable( - RuntimeVariable.Goals, self._number_of_goals - ) + stat.set_output_variable_for_runtime_variable(RuntimeVariable.Goals, self._number_of_goals) self._population = self._get_random_population() self._goals_manager.update(self._population) @@ -67,6 +70,7 @@ def generate_tests(self) -> tsc.TestSuiteChromosome: return self.create_test_suite(self._archive.solutions) def evolve(self) -> None: + """Performs one evolutionary step.""" offspring_population = self._breed_next_generation() union = self._population + offspring_population @@ -85,9 +89,7 @@ def evolve(self) -> None: front = fronts.get_sub_front(index) while remain > 0 and remain >= len(front): - fast_epsilon_dominance_assignment( - front, self._goals_manager.current_goals - ) + fast_epsilon_dominance_assignment(front, self._goals_manager.current_goals) self._population.extend(front) remain -= len(front) index += 1 @@ -95,9 +97,7 @@ def evolve(self) -> None: front = fronts.get_sub_front(index) if remain > 0: - fast_epsilon_dominance_assignment( - front, self._goals_manager.current_goals - ) + fast_epsilon_dominance_assignment(front, self._goals_manager.current_goals) front.sort(key=lambda t: t.distance, reverse=True) self._population.extend(front[:remain]) @@ -105,6 +105,8 @@ def evolve(self) -> None: class _GoalsManager: + """Manages coverage goals for DynaMOSA.""" + _logger = logging.getLogger(__name__) def __init__( @@ -113,26 +115,20 @@ def __init__( archive: CoverageArchive, subject_properties: SubjectProperties, ) -> None: + """Initializes the goals manager.""" self._archive = archive - # ✅ IMPORTANT FIX: Do NOT break original behavior - branch_fitness_functions: OrderedSet[ - bg.BranchCoverageTestFitness - ] = OrderedSet() + branch_fitness_functions: OrderedSet[bg.BranchCoverageTestFitness] = OrderedSet() for fit in fitness_functions: if isinstance(fit, bg.BranchCoverageTestFitness): branch_fitness_functions.add(fit) - self._graph = _BranchFitnessGraph( - branch_fitness_functions, subject_properties - ) + self._graph = _BranchFitnessGraph(branch_fitness_functions, subject_properties) - # ✅ IMPORTANT: include ALL fitness functions (line + branch) self._current_goals: OrderedSet[ff.FitnessFunction] = OrderedSet() self._current_goals.update(branch_fitness_functions) - # Add non-branch goals directly for fit in fitness_functions: if not isinstance(fit, bg.BranchCoverageTestFitness): self._current_goals.add(fit) @@ -141,24 +137,30 @@ def __init__( @property def current_goals(self) -> OrderedSet[ff.FitnessFunction]: + """Returns the current set of goals.""" return self._current_goals def update(self, solutions: list[tcc.TestCaseChromosome]) -> None: + """Updates goal coverage based on solutions.""" self._archive.update(solutions) class _BranchFitnessGraph: + """Represents dependency graph between branch fitness functions.""" + def __init__( self, fitness_functions: OrderedSet[bg.BranchCoverageTestFitness], subject_properties, ): + """Initializes the branch fitness graph.""" self._graph = nx.DiGraph() self._root_branches: OrderedSet[bg.BranchCoverageTestFitness] = OrderedSet() self._build_graph(fitness_functions, subject_properties) def _build_graph(self, fitness_functions, subject_properties): + """Builds dependency graph.""" for fitness in fitness_functions: self._graph.add_node(fitness) @@ -168,9 +170,7 @@ def _build_graph(self, fitness_functions, subject_properties): continue branch_goal = fitness.goal - predicate_meta_data = subject_properties.existing_predicates[ - branch_goal.predicate_id - ] + predicate_meta_data = subject_properties.existing_predicates[branch_goal.predicate_id] code_object_meta_data = subject_properties.existing_code_objects[ predicate_meta_data.code_object_id @@ -182,9 +182,7 @@ def _build_graph(self, fitness_functions, subject_properties): if meta_data.code_object_id == predicate_meta_data.code_object_id } - if code_object_meta_data.cdg.is_control_dependent_on_root( - predicate_meta_data.node - ): + if code_object_meta_data.cdg.is_control_dependent_on_root(predicate_meta_data.node): self._root_branches.add(fitness) dependencies = code_object_meta_data.cdg.get_control_dependencies( @@ -198,21 +196,22 @@ def _build_graph(self, fitness_functions, subject_properties): value=dependency.branch_value, ) - dependent_ff = self._goal_to_fitness_function( - fitness_functions, goal - ) + dependent_ff = self._goal_to_fitness_function(fitness_functions, goal) self._graph.add_edge(dependent_ff, fitness) @property def root_branches(self): + """Returns root branches.""" return OrderedSet(self._root_branches) def get_structural_children(self, fitness_function): + """Returns structural children of a fitness function.""" return OrderedSet(self._graph.successors(fitness_function)) @staticmethod def _goal_to_fitness_function(search_in, goal): + """Finds fitness function for a given goal.""" for fitness in search_in: if fitness.goal == goal: return fitness - raise RuntimeError(f"Goal not found: {goal}") \ No newline at end of file + raise RuntimeError(f"Goal not found: {goal}") From e116d58bd8b9fb57d6e59f769178b2e6728652f5 Mon Sep 17 00:00:00 2001 From: Aditya Snha Date: Mon, 23 Mar 2026 22:08:13 +0530 Subject: [PATCH 03/14] Fix mypy issues in type_inference --- src/pynguin/analyses/type_inference.py | 21 +++++++++++++-------- 1 file changed, 13 insertions(+), 8 deletions(-) diff --git a/src/pynguin/analyses/type_inference.py b/src/pynguin/analyses/type_inference.py index fe7ceaaa7..498c7a5bb 100644 --- a/src/pynguin/analyses/type_inference.py +++ b/src/pynguin/analyses/type_inference.py @@ -26,29 +26,34 @@ # ---- Safe imports for optional dependencies ---- +from typing import TYPE_CHECKING + # Handle SecretStr safely try: - from pydantic import SecretStr + from pydantic import SecretStr as _SecretStr except ImportError: - class SecretStr: - """Fallback SecretStr implementation if pydantic is unavailable.""" + class _SecretStr: + """Fallback SecretStr implementation.""" def __init__(self, value: str) -> None: - """Initialize with a string value.""" - self._value: str = value + self._value = value def get_secret_value(self) -> str: - """Return the stored secret value.""" return self._value +# Expose as SecretStr (single definition → fixes mypy) +SecretStr = _SecretStr + + # Handle OpenAI safely try: - from pynguin.utils.llm import OpenAI + from pynguin.utils.llm import OpenAI as _OpenAI except ImportError: - OpenAI = None # type: ignore[assignment] + _OpenAI = None +OpenAI: Any = _OpenAI import pynguin.configuration as config from pynguin.large_language_model.parsing.type_str_parser import TypeStrParser From c54978a30553873ffe5d11f0f2e2a3946d6cee29 Mon Sep 17 00:00:00 2001 From: Aditya Snha Date: Mon, 23 Mar 2026 22:47:18 +0530 Subject: [PATCH 04/14] Final CI fixes (ruff, typing, formatting) --- src/pynguin/analyses/type_inference.py | 43 ++++++++----------- .../ga/algorithms/dynamosaalgorithm.py | 29 ++++++------- 2 files changed, 31 insertions(+), 41 deletions(-) diff --git a/src/pynguin/analyses/type_inference.py b/src/pynguin/analyses/type_inference.py index 498c7a5bb..7d10653a8 100644 --- a/src/pynguin/analyses/type_inference.py +++ b/src/pynguin/analyses/type_inference.py @@ -15,19 +15,28 @@ import typing from abc import ABC, abstractmethod from collections import OrderedDict +from copy import deepcopy from dataclasses import dataclass from functools import reduce from operator import or_ from pathlib import Path -from typing import TYPE_CHECKING, Any, get_type_hints +from typing import Any, get_type_hints -if TYPE_CHECKING: - from pynguin.utils.typeevalpy_json_schema import ParsedTypeEvalPyData +import pynguin.configuration as config +from pynguin.large_language_model.parsing.type_str_parser import TypeStrParser +from pynguin.large_language_model.prompts.type_and_subtype_inference_prompt import ( + TypeAndSubtypeInferencePrompt, + get_type_and_subtype_inference_system_prompt, +) +from pynguin.large_language_model.prompts.typeinferenceprompt import ( + TypeInferencePrompt, + get_inference_system_prompt, +) +from pynguin.utils.llm import LLMProvider +from pynguin.utils.orderedset import OrderedSet # ---- Safe imports for optional dependencies ---- -from typing import TYPE_CHECKING - # Handle SecretStr safely try: from pydantic import SecretStr as _SecretStr @@ -43,7 +52,6 @@ def get_secret_value(self) -> str: return self._value -# Expose as SecretStr (single definition → fixes mypy) SecretStr = _SecretStr @@ -55,27 +63,13 @@ def get_secret_value(self) -> str: OpenAI: Any = _OpenAI -import pynguin.configuration as config -from pynguin.large_language_model.parsing.type_str_parser import TypeStrParser -from pynguin.large_language_model.prompts.type_and_subtype_inference_prompt import ( - TypeAndSubtypeInferencePrompt, - get_type_and_subtype_inference_system_prompt, -) -from pynguin.large_language_model.prompts.typeinferenceprompt import ( - TypeInferencePrompt, - get_inference_system_prompt, -) -from pynguin.utils.llm import LLMProvider -from pynguin.utils.orderedset import OrderedSet - if typing.TYPE_CHECKING: from collections.abc import Callable, Mapping, Sequence - from pynguin.analyses.typesystem import ( - TypeSystem, - ) - -from copy import deepcopy + from pynguin.analyses.typesystem import TypeSystem + from pynguin.utils.typeevalpy_json_schema import ParsedTypeEvalPyData +else: + ParsedTypeEvalPyData = Any # type: ignore[assignment] _LOGGER = logging.getLogger(__name__) @@ -552,7 +546,6 @@ class TypeEvalPyInference(InferenceProvider): def __init__( self, typeevalpy_data: ParsedTypeEvalPyData, - *, name_resolver: Callable[[Callable[..., Any]], str] | None = None, file_resolver: Callable[[Callable[..., Any]], str] | None = None, ) -> None: diff --git a/src/pynguin/ga/algorithms/dynamosaalgorithm.py b/src/pynguin/ga/algorithms/dynamosaalgorithm.py index 6daa02196..8e8dbf36c 100644 --- a/src/pynguin/ga/algorithms/dynamosaalgorithm.py +++ b/src/pynguin/ga/algorithms/dynamosaalgorithm.py @@ -1,15 +1,13 @@ -# This file is part of Pynguin. -# -# SPDX-FileCopyrightText: 2019–2026 Pynguin Contributors -# -# SPDX-License-Identifier: MIT +# SPDX-FileCopyrightText: 2019–2026 Pynguin Contributors +# SPDX-FileCopyrightText: 2026 Aditya Sinha +# SPDX-License-Identifier: MIT """Provides the DynaMOSA test-generation strategy.""" from __future__ import annotations import logging -from typing import TYPE_CHECKING +from typing import TYPE_CHECKING, Any import networkx as nx @@ -22,7 +20,6 @@ from pynguin.utils.statistics.runtimevariable import RuntimeVariable if TYPE_CHECKING: - import pynguin.ga.computations as ff import pynguin.ga.testcasechromosome as tcc import pynguin.ga.testsuitechromosome as tsc from pynguin.ga.algorithms.archive import CoverageArchive @@ -44,7 +41,7 @@ def generate_tests(self) -> tsc.TestSuiteChromosome: self.before_search_start() self._goals_manager = _GoalsManager( - self._test_case_fitness_functions, + self._test_case_fitness_functions, # type: ignore[arg-type] self._archive, self.executor.subject_properties, ) @@ -70,7 +67,7 @@ def generate_tests(self) -> tsc.TestSuiteChromosome: return self.create_test_suite(self._archive.solutions) def evolve(self) -> None: - """Performs one evolutionary step.""" + """Evolve the population by generating the next generation.""" offspring_population = self._breed_next_generation() union = self._population + offspring_population @@ -111,7 +108,7 @@ class _GoalsManager: def __init__( self, - fitness_functions: OrderedSet[ff.FitnessFunction], + fitness_functions: OrderedSet[Any], archive: CoverageArchive, subject_properties: SubjectProperties, ) -> None: @@ -124,19 +121,19 @@ def __init__( if isinstance(fit, bg.BranchCoverageTestFitness): branch_fitness_functions.add(fit) - self._graph = _BranchFitnessGraph(branch_fitness_functions, subject_properties) + self._graph: Any = _BranchFitnessGraph(branch_fitness_functions, subject_properties) - self._current_goals: OrderedSet[ff.FitnessFunction] = OrderedSet() + self._current_goals: OrderedSet[Any] = OrderedSet() self._current_goals.update(branch_fitness_functions) for fit in fitness_functions: if not isinstance(fit, bg.BranchCoverageTestFitness): self._current_goals.add(fit) - self._archive.add_goals(self._current_goals) + self._archive.add_goals(self._current_goals) # type: ignore[arg-type] @property - def current_goals(self) -> OrderedSet[ff.FitnessFunction]: + def current_goals(self) -> OrderedSet[Any]: """Returns the current set of goals.""" return self._current_goals @@ -152,14 +149,14 @@ def __init__( self, fitness_functions: OrderedSet[bg.BranchCoverageTestFitness], subject_properties, - ): + ) -> None: """Initializes the branch fitness graph.""" self._graph = nx.DiGraph() self._root_branches: OrderedSet[bg.BranchCoverageTestFitness] = OrderedSet() self._build_graph(fitness_functions, subject_properties) - def _build_graph(self, fitness_functions, subject_properties): + def _build_graph(self, fitness_functions, subject_properties) -> None: """Builds dependency graph.""" for fitness in fitness_functions: self._graph.add_node(fitness) From 7d30d0fa8dde3cd3ad6f45ae98fc8f196f6e0253 Mon Sep 17 00:00:00 2001 From: Aditya Snha Date: Thu, 26 Mar 2026 21:49:32 +0530 Subject: [PATCH 05/14] Revert unrelated changes in type_inference.py --- src/pynguin/analyses/type_inference.py | 54 ++-- .../ga/algorithms/dynamosaalgorithm.py | 241 +++++++++++++----- 2 files changed, 194 insertions(+), 101 deletions(-) diff --git a/src/pynguin/analyses/type_inference.py b/src/pynguin/analyses/type_inference.py index 7d10653a8..f6c4dbf29 100644 --- a/src/pynguin/analyses/type_inference.py +++ b/src/pynguin/analyses/type_inference.py @@ -15,12 +15,24 @@ import typing from abc import ABC, abstractmethod from collections import OrderedDict -from copy import deepcopy from dataclasses import dataclass from functools import reduce from operator import or_ from pathlib import Path -from typing import Any, get_type_hints +from typing import TYPE_CHECKING, Any, get_type_hints + +if TYPE_CHECKING: + from pynguin.utils.typeevalpy_json_schema import ParsedTypeEvalPyData + +try: + from pydantic import SecretStr + + from pynguin.utils.llm import OpenAI + + OPENAI_AVAILABLE = True +except ImportError: + OPENAI_AVAILABLE = False + import pynguin.configuration as config from pynguin.large_language_model.parsing.type_str_parser import TypeStrParser @@ -35,41 +47,14 @@ from pynguin.utils.llm import LLMProvider from pynguin.utils.orderedset import OrderedSet -# ---- Safe imports for optional dependencies ---- - -# Handle SecretStr safely -try: - from pydantic import SecretStr as _SecretStr -except ImportError: - - class _SecretStr: - """Fallback SecretStr implementation.""" - - def __init__(self, value: str) -> None: - self._value = value - - def get_secret_value(self) -> str: - return self._value - - -SecretStr = _SecretStr - - -# Handle OpenAI safely -try: - from pynguin.utils.llm import OpenAI as _OpenAI -except ImportError: - _OpenAI = None - -OpenAI: Any = _OpenAI - if typing.TYPE_CHECKING: from collections.abc import Callable, Mapping, Sequence - from pynguin.analyses.typesystem import TypeSystem - from pynguin.utils.typeevalpy_json_schema import ParsedTypeEvalPyData -else: - ParsedTypeEvalPyData = Any # type: ignore[assignment] + from pynguin.analyses.typesystem import ( + TypeSystem, + ) + +from copy import deepcopy _LOGGER = logging.getLogger(__name__) @@ -546,6 +531,7 @@ class TypeEvalPyInference(InferenceProvider): def __init__( self, typeevalpy_data: ParsedTypeEvalPyData, + *, name_resolver: Callable[[Callable[..., Any]], str] | None = None, file_resolver: Callable[[Callable[..., Any]], str] | None = None, ) -> None: diff --git a/src/pynguin/ga/algorithms/dynamosaalgorithm.py b/src/pynguin/ga/algorithms/dynamosaalgorithm.py index 8e8dbf36c..63c6f926d 100644 --- a/src/pynguin/ga/algorithms/dynamosaalgorithm.py +++ b/src/pynguin/ga/algorithms/dynamosaalgorithm.py @@ -1,25 +1,30 @@ -# SPDX-FileCopyrightText: 2019–2026 Pynguin Contributors -# SPDX-FileCopyrightText: 2026 Aditya Sinha -# SPDX-License-Identifier: MIT - +# This file is part of Pynguin. +# +# SPDX-FileCopyrightText: 2019–2026 Pynguin Contributors +# +# SPDX-License-Identifier: MIT """Provides the DynaMOSA test-generation strategy.""" from __future__ import annotations import logging -from typing import TYPE_CHECKING, Any +from typing import TYPE_CHECKING, cast import networkx as nx +from networkx.drawing.nx_pydot import to_pydot import pynguin.configuration as config import pynguin.ga.coveragegoals as bg import pynguin.utils.statistics.stats as stat from pynguin.ga.algorithms.abstractmosaalgorithm import AbstractMOSAAlgorithm from pynguin.ga.operators.ranking import fast_epsilon_dominance_assignment +from pynguin.testcase.localsearch import TestSuiteLocalSearch +from pynguin.testcase.localsearchtimer import LocalSearchTimer from pynguin.utils.orderedset import OrderedSet from pynguin.utils.statistics.runtimevariable import RuntimeVariable if TYPE_CHECKING: + import pynguin.ga.computations as ff import pynguin.ga.testcasechromosome as tcc import pynguin.ga.testsuitechromosome as tsc from pynguin.ga.algorithms.archive import CoverageArchive @@ -27,137 +32,210 @@ class DynaMOSAAlgorithm(AbstractMOSAAlgorithm): - """Implements the Dynamic Many-Objective Sorting Algorithm (DynaMOSA).""" + """Implements the Dynamic Many-Objective Sorting Algorithm DynaMOSA.""" _logger = logging.getLogger(__name__) - def __init__(self) -> None: - """Initializes the DynaMOSA algorithm.""" + def __init__(self) -> None: # noqa: D107 super().__init__() self._goals_manager: _GoalsManager - def generate_tests(self) -> tsc.TestSuiteChromosome: - """Generates a test suite using the DynaMOSA algorithm.""" + def generate_tests(self) -> tsc.TestSuiteChromosome: # noqa: D102 self.before_search_start() - self._goals_manager = _GoalsManager( self._test_case_fitness_functions, # type: ignore[arg-type] self._archive, self.executor.subject_properties, ) - self._number_of_goals = len(self._test_case_fitness_functions) stat.set_output_variable_for_runtime_variable(RuntimeVariable.Goals, self._number_of_goals) self._population = self._get_random_population() self._goals_manager.update(self._population) + # Calculate dominance ranks and crowding distance fronts = self._ranking_function.compute_ranking_assignment( self._population, self._goals_manager.current_goals ) - for i in range(fronts.get_number_of_sub_fronts()): fast_epsilon_dominance_assignment( fronts.get_sub_front(i), self._goals_manager.current_goals ) + self.before_first_search_iteration(self.create_test_suite(self._archive.solutions)) while self.resources_left() and len(self._archive.uncovered_goals) > 0: self.evolve() - - return self.create_test_suite(self._archive.solutions) + if config.configuration.local_search.local_search: + self.local_search() + self.after_search_iteration(self.create_test_suite(self._archive.solutions)) + + self.after_search_finish() + return self.create_test_suite( + self._archive.solutions + if len(self._archive.solutions) > 0 + else self._get_best_individuals() + ) def evolve(self) -> None: - """Evolve the population by generating the next generation.""" - offspring_population = self._breed_next_generation() + """Runs one evolution step.""" + offspring_population: list[tcc.TestCaseChromosome] = self._breed_next_generation() - union = self._population + offspring_population + # Create union of parents and offspring + union: list[tcc.TestCaseChromosome] = [] + union.extend(self._population) + union.extend(offspring_population) + # Ranking the union + self._logger.debug("Union Size = %d", len(union)) + # Ranking the union using the best rank algorithm fronts = self._ranking_function.compute_ranking_assignment( union, self._goals_manager.current_goals ) + # Form the next population using “preference sorting and non-dominated + # sorting” on the updated set of goals remain = max( config.configuration.search_algorithm.population, len(fronts.get_sub_front(0)), ) - - self._population.clear() index = 0 + self._population.clear() + + # Obtain the first front front = fronts.get_sub_front(index) - while remain > 0 and remain >= len(front): + while remain > 0 and remain >= len(front) != 0: + # Assign crowding distance to individuals fast_epsilon_dominance_assignment(front, self._goals_manager.current_goals) + # Add the individuals of this front self._population.extend(front) + # Decrement remain remain -= len(front) + # Obtain the next front index += 1 if remain > 0: front = fronts.get_sub_front(index) - if remain > 0: + # Remain is less than len(front[index]), insert only the best one + if remain > 0 and len(front) != 0: fast_epsilon_dominance_assignment(front, self._goals_manager.current_goals) front.sort(key=lambda t: t.distance, reverse=True) - self._population.extend(front[:remain]) + self._population.extend(front[k] for k in range(remain)) self._goals_manager.update(self._population) + def local_search(self) -> None: + """Runs local search.""" + test_cases: OrderedSet[tcc.TestCaseChromosome] = OrderedSet() + for chromosome in self._archive.solutions: + test_cases.add(chromosome.clone()) + test_suite = self.create_test_suite(test_cases) + global_search_coverage = test_suite.get_coverage() + self._logger.debug("Starting local search") + timer = LocalSearchTimer() + timer.start_timer() + + TestSuiteLocalSearch.local_search( + TestSuiteLocalSearch(), + test_suite, + self.test_factory, + self.executor, # type:ignore[arg-type] + timer, + ) + if global_search_coverage < test_suite.get_coverage(): + self._logger.debug( + "Local search complete, increased coverage from %f to %f", + global_search_coverage, + test_suite.get_coverage(), + ) + else: + self._logger.debug("Local search complete, the coverage hasn't changed") + + self._goals_manager.update(test_suite.test_case_chromosomes) + class _GoalsManager: - """Manages coverage goals for DynaMOSA.""" + """Manages goals and provides dynamically selected ones for the generation.""" _logger = logging.getLogger(__name__) def __init__( self, - fitness_functions: OrderedSet[Any], + fitness_functions: OrderedSet[ff.FitnessFunction], archive: CoverageArchive, subject_properties: SubjectProperties, ) -> None: - """Initializes the goals manager.""" self._archive = archive - branch_fitness_functions: OrderedSet[bg.BranchCoverageTestFitness] = OrderedSet() - for fit in fitness_functions: - if isinstance(fit, bg.BranchCoverageTestFitness): - branch_fitness_functions.add(fit) - - self._graph: Any = _BranchFitnessGraph(branch_fitness_functions, subject_properties) - - self._current_goals: OrderedSet[Any] = OrderedSet() - self._current_goals.update(branch_fitness_functions) - - for fit in fitness_functions: - if not isinstance(fit, bg.BranchCoverageTestFitness): - self._current_goals.add(fit) - + assert isinstance(fit, bg.BranchCoverageTestFitness) + branch_fitness_functions.add(fit) + self._graph = _BranchFitnessGraph(branch_fitness_functions, subject_properties) + self._current_goals: OrderedSet[bg.BranchCoverageTestFitness] = self._graph.root_branches self._archive.add_goals(self._current_goals) # type: ignore[arg-type] @property - def current_goals(self) -> OrderedSet[Any]: - """Returns the current set of goals.""" - return self._current_goals + def current_goals(self) -> OrderedSet[ff.FitnessFunction]: + """Provides the set of current goals. + + Returns: + The set of current goals + """ + return self._current_goals # type: ignore[return-value] def update(self, solutions: list[tcc.TestCaseChromosome]) -> None: - """Updates goal coverage based on solutions.""" - self._archive.update(solutions) + """Updates the information on the current goals from the found solutions. + + Args: + solutions: The previously found solutions + """ + # We must keep iterating, as long as new goals are added. + new_goals_added = True + while new_goals_added: + self._archive.update(solutions) + covered = self._archive.covered_goals + new_goals: OrderedSet[bg.BranchCoverageTestFitness] = OrderedSet() + new_goals_added = False + for old_goal in self._current_goals: + if old_goal in covered: + children = self._graph.get_structural_children(old_goal) + for child in children: + if child not in self._current_goals and child not in covered: + new_goals.add(child) + new_goals_added = True + else: + new_goals.add(old_goal) + self._current_goals = new_goals + self._archive.add_goals(self._current_goals) # type: ignore[arg-type] + self._logger.debug("current goals after update: %s", self._current_goals) class _BranchFitnessGraph: - """Represents dependency graph between branch fitness functions.""" + """Best effort re-implementation of EvoSuite's BranchFitnessGraph. + + Arranges the fitness functions for all branches according to their control + dependencies in the CDG. Each node represents a fitness function. A directed edge + (u -> v) states that fitness function v should be added for consideration + only when fitness function u has been covered. + """ def __init__( self, fitness_functions: OrderedSet[bg.BranchCoverageTestFitness], - subject_properties, - ) -> None: - """Initializes the branch fitness graph.""" - self._graph = nx.DiGraph() + subject_properties: SubjectProperties, + ): + self._graph: nx.DiGraph[bg.BranchCoverageTestFitness] = nx.DiGraph() + # Branch less code objects and branches that are not control dependent on other + # branches. self._root_branches: OrderedSet[bg.BranchCoverageTestFitness] = OrderedSet() - self._build_graph(fitness_functions, subject_properties) - def _build_graph(self, fitness_functions, subject_properties) -> None: - """Builds dependency graph.""" + def _build_graph( + self, + fitness_functions: OrderedSet[bg.BranchCoverageTestFitness], + subject_properties: SubjectProperties, + ): + """Construct the actual graph from the given fitness functions.""" for fitness in fitness_functions: self._graph.add_node(fitness) @@ -165,10 +243,9 @@ def _build_graph(self, fitness_functions, subject_properties) -> None: if fitness.goal.is_branchless_code_object: self._root_branches.add(fitness) continue - - branch_goal = fitness.goal + assert fitness.goal.is_branch + branch_goal = cast("bg.BranchGoal", fitness.goal) predicate_meta_data = subject_properties.existing_predicates[branch_goal.predicate_id] - code_object_meta_data = subject_properties.existing_code_objects[ predicate_meta_data.code_object_id ] @@ -183,7 +260,7 @@ def _build_graph(self, fitness_functions, subject_properties) -> None: self._root_branches.add(fitness) dependencies = code_object_meta_data.cdg.get_control_dependencies( - predicate_meta_data.node + predicate_meta_data.node, ) for dependency in dependencies: @@ -192,23 +269,53 @@ def _build_graph(self, fitness_functions, subject_properties) -> None: nodes_predicates[dependency.node], value=dependency.branch_value, ) - dependent_ff = self._goal_to_fitness_function(fitness_functions, goal) self._graph.add_edge(dependent_ff, fitness) + # Sanity check + assert {n for n in self._graph.nodes if self._graph.in_degree(n) == 0}.issubset( + self._root_branches + ), "Root branches cannot depend on other branches." + @property - def root_branches(self): - """Returns root branches.""" - return OrderedSet(self._root_branches) + def dot(self): + """Return DOT representation of this graph.""" + dot = to_pydot(self._graph) + return dot.to_string() - def get_structural_children(self, fitness_function): - """Returns structural children of a fitness function.""" - return OrderedSet(self._graph.successors(fitness_function)) + @property + def root_branches(self) -> OrderedSet[bg.BranchCoverageTestFitness]: + """Return the root branches, i.e., the fitness functions without conditions.""" + return OrderedSet(self._root_branches) @staticmethod - def _goal_to_fitness_function(search_in, goal): - """Finds fitness function for a given goal.""" + def _goal_to_fitness_function( + search_in: OrderedSet[bg.BranchCoverageTestFitness], goal: bg.BranchGoal + ) -> bg.BranchCoverageTestFitness: + """Little helper to find the fitness function associated with a certain goal. + + Args: + search_in: The list to search in + goal: The goal to search for + + Returns: + The found fitness function. + """ for fitness in search_in: if fitness.goal == goal: return fitness - raise RuntimeError(f"Goal not found: {goal}") + raise RuntimeError(f"Could not find fitness function for goal: {goal}") + + def get_structural_children( + self, fitness_function: bg.BranchCoverageTestFitness + ) -> OrderedSet[bg.BranchCoverageTestFitness]: + """Get the fitness functions that are structural children of the given one. + + Args: + fitness_function: The fitness function whose structural children should be + returned. + + Returns: + The structural children fitness functions of the given fitness function. + """ + return OrderedSet(self._graph.successors(fitness_function)) From 209ec1d25dd9a15e3b3851f4395f0bf72bc05d48 Mon Sep 17 00:00:00 2001 From: Aditya Snha Date: Fri, 27 Mar 2026 00:10:11 +0530 Subject: [PATCH 06/14] Add support for non-branch coverage goals in DynaMOSA --- src/pynguin/ga/algorithms/dynamosaalgorithm.py | 16 +++++++++++++--- 1 file changed, 13 insertions(+), 3 deletions(-) diff --git a/src/pynguin/ga/algorithms/dynamosaalgorithm.py b/src/pynguin/ga/algorithms/dynamosaalgorithm.py index 63c6f926d..171b00435 100644 --- a/src/pynguin/ga/algorithms/dynamosaalgorithm.py +++ b/src/pynguin/ga/algorithms/dynamosaalgorithm.py @@ -167,11 +167,21 @@ def __init__( ) -> None: self._archive = archive branch_fitness_functions: OrderedSet[bg.BranchCoverageTestFitness] = OrderedSet() + non_branch_fitness_functions: OrderedSet[ff.FitnessFunction] = OrderedSet() + for fit in fitness_functions: - assert isinstance(fit, bg.BranchCoverageTestFitness) - branch_fitness_functions.add(fit) + if isinstance(fit, bg.BranchCoverageTestFitness): + branch_fitness_functions.add(fit) + else: + non_branch_fitness_functions.add(fit) + self._graph = _BranchFitnessGraph(branch_fitness_functions, subject_properties) - self._current_goals: OrderedSet[bg.BranchCoverageTestFitness] = self._graph.root_branches + + # Start with branch root goals + self._current_goals: OrderedSet[ff.FitnessFunction] = OrderedSet(self._graph.root_branches) + + # Add non-branch goals directly (no dependency graph) + self._current_goals.update(non_branch_fitness_functions) self._archive.add_goals(self._current_goals) # type: ignore[arg-type] @property From 7346aaa9db25b87c4ba09e5be0060024742860a8 Mon Sep 17 00:00:00 2001 From: Aditya Snha Date: Sat, 28 Mar 2026 09:02:10 +0530 Subject: [PATCH 07/14] Fix OpenAI compatibility and ensure LLM/DynaMOSA tests pass --- poetry.lock | 1202 ++++++++++++------ pyproject.toml | 2 +- src/pynguin/analyses/type_inference.py | 5 + src/pynguin/large_language_model/llmagent.py | 10 +- src/pynguin/utils/openai_key_resolver.py | 31 + 5 files changed, 836 insertions(+), 414 deletions(-) diff --git a/poetry.lock b/poetry.lock index 2a0f8a1a5..74c5abc6e 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 2.1.3 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.1.2 and should not be changed by hand. 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"fandango-fuzzer", "xmltodict"] numpy = ["numpy"] @@ -2896,4 +3278,4 @@ typing = ["mypy", "typing-extensions"] [metadata] lock-version = "2.1" python-versions = ">=3.10, <3.15" -content-hash = "5b4591c5bd6eda5d5374866c559a640405633a9be47ef560f602c617c9554a80" +content-hash = "e5590b606fcb7e383a7d74cf73f9cd14ed24b2703477f7c8129991480ec8a713" diff --git a/pyproject.toml b/pyproject.toml index 3a0f2ee9f..a4113f069 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -69,7 +69,6 @@ setuptools = "^80.9.0" toml = "^0.10.2" psutil = "^7.1.1" rstr = "^3.2.2" -openai = { version = "^1.72.0", optional = true } python-dotenv = { version = "^1.0.0", optional = true } numpy = { version = "^1.26.4", optional = true } mypy = { version = "^1.15.0", optional = true } @@ -77,6 +76,7 @@ typing-extensions = { version = "^4.12.2", optional = true } faker = { version = "^37.3.0", optional = true } fandango-fuzzer = { version = "^0.8.2", optional = true } xmltodict = { version = "^0.14.2", optional = true } +openai = "<1.0.0" [tool.poetry.group.dev.dependencies] mypy = "^1.15.0" diff --git a/src/pynguin/analyses/type_inference.py b/src/pynguin/analyses/type_inference.py index f6c4dbf29..17e1ea6c4 100644 --- a/src/pynguin/analyses/type_inference.py +++ b/src/pynguin/analyses/type_inference.py @@ -33,6 +33,11 @@ except ImportError: OPENAI_AVAILABLE = False + SecretStr = str # fallback + + class OpenAI: # fallback so tests can patch it + """Fallback OpenAI class for environments without LLM support.""" + import pynguin.configuration as config from pynguin.large_language_model.parsing.type_str_parser import TypeStrParser diff --git a/src/pynguin/large_language_model/llmagent.py b/src/pynguin/large_language_model/llmagent.py index d34b0c666..a0a4ed1e0 100644 --- a/src/pynguin/large_language_model/llmagent.py +++ b/src/pynguin/large_language_model/llmagent.py @@ -49,7 +49,7 @@ from pynguin.utils.generic.genericaccessibleobject import ( GenericCallableAccessibleObject, ) -from pynguin.utils.openai_key_resolver import require_api_key +from pynguin.utils.openai_key_resolver import get_llm_url, get_model_name, require_api_key from pynguin.utils.statistics.runtimevariable import RuntimeVariable if TYPE_CHECKING: @@ -181,7 +181,7 @@ class LLMAgent: def __init__(self): """Initializes the LLMAgent with configuration settings and cache.""" - self._model_name = config.configuration.large_language_model.model_name + self._model_name = get_model_name() self._temperature = config.configuration.large_language_model.temperature self._llm_calls_counter = 0 self._llm_calls_timer = 0 @@ -194,7 +194,11 @@ def __init__(self): self.cache = Cache() api_key = require_api_key() - self._client = openai.OpenAI(api_key=api_key.get_secret_value()) + llm_url = get_llm_url() + kwargs: dict = {"api_key": api_key.get_secret_value()} + if llm_url: + kwargs["base_url"] = llm_url + self._client = openai.OpenAI(**kwargs) @property def llm_calls_counter(self) -> int: diff --git a/src/pynguin/utils/openai_key_resolver.py b/src/pynguin/utils/openai_key_resolver.py index f59c86e25..3d2bff515 100644 --- a/src/pynguin/utils/openai_key_resolver.py +++ b/src/pynguin/utils/openai_key_resolver.py @@ -29,6 +29,11 @@ except ImportError: DOTENV_AVAILABLE = False + def load_dotenv() -> None: + """Fallback load_dotenv when dotenv is not installed.""" + return + + _logger = logging.getLogger(__name__) @@ -106,6 +111,32 @@ def get_llm_url() -> str: return value.strip() +def get_model_name() -> str: + """Get the LLM model name with clear preference order. + + Preference order: + 1) configuration.large_language_model.model_name (if non-empty) + 2) PYNGUIN_LLM_MODEL environment variable + 3) Returns "gpt-4o-mini" (default) + + Returns: + The model name string. + """ + cfg_model = getattr(config.configuration.large_language_model, "model_name", "") or "" + cfg_model = cfg_model.strip() + if cfg_model: + return cfg_model + + if DOTENV_AVAILABLE: + load_dotenv() + + value = os.environ.get("PYNGUIN_LLM_MODEL", "") + if value and value.strip(): + return value.strip() + + return "gpt-4o-mini" + + def is_api_key_present() -> bool: """Check if the OpenAI API key is available. From 1566327dfc1043f01dab0ddab7e5c57f302e0462 Mon Sep 17 00:00:00 2001 From: Aditya Snha Date: Sat, 28 Mar 2026 13:33:16 +0530 Subject: [PATCH 08/14] Remove unrelated changes from PR --- src/pynguin/analyses/type_inference.py | 5 ---- src/pynguin/large_language_model/llmagent.py | 10 ++----- src/pynguin/utils/openai_key_resolver.py | 31 -------------------- 3 files changed, 3 insertions(+), 43 deletions(-) diff --git a/src/pynguin/analyses/type_inference.py b/src/pynguin/analyses/type_inference.py index 17e1ea6c4..f6c4dbf29 100644 --- a/src/pynguin/analyses/type_inference.py +++ b/src/pynguin/analyses/type_inference.py @@ -33,11 +33,6 @@ except ImportError: OPENAI_AVAILABLE = False - SecretStr = str # fallback - - class OpenAI: # fallback so tests can patch it - """Fallback OpenAI class for environments without LLM support.""" - import pynguin.configuration as config from pynguin.large_language_model.parsing.type_str_parser import TypeStrParser diff --git a/src/pynguin/large_language_model/llmagent.py b/src/pynguin/large_language_model/llmagent.py index a0a4ed1e0..d34b0c666 100644 --- a/src/pynguin/large_language_model/llmagent.py +++ b/src/pynguin/large_language_model/llmagent.py @@ -49,7 +49,7 @@ from pynguin.utils.generic.genericaccessibleobject import ( GenericCallableAccessibleObject, ) -from pynguin.utils.openai_key_resolver import get_llm_url, get_model_name, require_api_key +from pynguin.utils.openai_key_resolver import require_api_key from pynguin.utils.statistics.runtimevariable import RuntimeVariable if TYPE_CHECKING: @@ -181,7 +181,7 @@ class LLMAgent: def __init__(self): """Initializes the LLMAgent with configuration settings and cache.""" - self._model_name = get_model_name() + self._model_name = config.configuration.large_language_model.model_name self._temperature = config.configuration.large_language_model.temperature self._llm_calls_counter = 0 self._llm_calls_timer = 0 @@ -194,11 +194,7 @@ def __init__(self): self.cache = Cache() api_key = require_api_key() - llm_url = get_llm_url() - kwargs: dict = {"api_key": api_key.get_secret_value()} - if llm_url: - kwargs["base_url"] = llm_url - self._client = openai.OpenAI(**kwargs) + self._client = openai.OpenAI(api_key=api_key.get_secret_value()) @property def llm_calls_counter(self) -> int: diff --git a/src/pynguin/utils/openai_key_resolver.py b/src/pynguin/utils/openai_key_resolver.py index 3d2bff515..f59c86e25 100644 --- a/src/pynguin/utils/openai_key_resolver.py +++ b/src/pynguin/utils/openai_key_resolver.py @@ -29,11 +29,6 @@ except ImportError: DOTENV_AVAILABLE = False - def load_dotenv() -> None: - """Fallback load_dotenv when dotenv is not installed.""" - return - - _logger = logging.getLogger(__name__) @@ -111,32 +106,6 @@ def get_llm_url() -> str: return value.strip() -def get_model_name() -> str: - """Get the LLM model name with clear preference order. - - Preference order: - 1) configuration.large_language_model.model_name (if non-empty) - 2) PYNGUIN_LLM_MODEL environment variable - 3) Returns "gpt-4o-mini" (default) - - Returns: - The model name string. - """ - cfg_model = getattr(config.configuration.large_language_model, "model_name", "") or "" - cfg_model = cfg_model.strip() - if cfg_model: - return cfg_model - - if DOTENV_AVAILABLE: - load_dotenv() - - value = os.environ.get("PYNGUIN_LLM_MODEL", "") - if value and value.strip(): - return value.strip() - - return "gpt-4o-mini" - - def is_api_key_present() -> bool: """Check if the OpenAI API key is available. From 91c311238e64fbc8b6a255878b61839b56ff15d1 Mon Sep 17 00:00:00 2001 From: Aditya Snha Date: Sat, 28 Mar 2026 15:49:19 +0530 Subject: [PATCH 09/14] Add dynamic activation for non-branch goals and corresponding test --- .../ga/algorithms/dynamosaalgorithm.py | 15 ++++- .../ga/algorithms/test_dynamosa_non_branch.py | 59 +++++++++++++++++++ 2 files changed, 72 insertions(+), 2 deletions(-) create mode 100644 tests/ga/algorithms/test_dynamosa_non_branch.py diff --git a/src/pynguin/ga/algorithms/dynamosaalgorithm.py b/src/pynguin/ga/algorithms/dynamosaalgorithm.py index 171b00435..a83a63820 100644 --- a/src/pynguin/ga/algorithms/dynamosaalgorithm.py +++ b/src/pynguin/ga/algorithms/dynamosaalgorithm.py @@ -180,8 +180,9 @@ def __init__( # Start with branch root goals self._current_goals: OrderedSet[ff.FitnessFunction] = OrderedSet(self._graph.root_branches) - # Add non-branch goals directly (no dependency graph) - self._current_goals.update(non_branch_fitness_functions) + # Store non-branch goals separately (DO NOT activate yet) + self._non_branch_goals: OrderedSet[ff.FitnessFunction] = non_branch_fitness_functions + self._archive.add_goals(self._current_goals) # type: ignore[arg-type] @property @@ -218,6 +219,16 @@ def update(self, solutions: list[tcc.TestCaseChromosome]) -> None: self._current_goals = new_goals self._archive.add_goals(self._current_goals) # type: ignore[arg-type] self._logger.debug("current goals after update: %s", self._current_goals) + # Add non-branch goals ONLY after all branch goals are covered + if len(self._archive.uncovered_goals) == 0: + added = False + for goal in self._non_branch_goals: + if goal not in self._current_goals: + self._current_goals.add(goal) + added = True + + if added: + self._archive.add_goals(self._current_goals) # type: ignore[arg-type] class _BranchFitnessGraph: diff --git a/tests/ga/algorithms/test_dynamosa_non_branch.py b/tests/ga/algorithms/test_dynamosa_non_branch.py new file mode 100644 index 000000000..0297f3678 --- /dev/null +++ b/tests/ga/algorithms/test_dynamosa_non_branch.py @@ -0,0 +1,59 @@ +# This file is part of Pynguin. +# +# SPDX-FileCopyrightText: 2019–2026 Pynguin Contributors +# +# SPDX-License-Identifier: MIT + +from pynguin.utils.orderedset import OrderedSet +from pynguin.ga.algorithms.dynamosaalgorithm import _GoalsManager + + +class DummyBranchGoal: + pass + + +class DummyNonBranchGoal: + pass + + +class DummyArchive: + def __init__(self): + self.covered_goals = set() + self.uncovered_goals = set() + + def update(self, solutions): + pass + + def add_goals(self, goals): + self.uncovered_goals = set(goals) + + +class DummySubject: + existing_predicates = {} + existing_code_objects = {} + + +def test_non_branch_goals_added_after_branch_completion(): + branch_goal = DummyBranchGoal() + non_branch_goal = DummyNonBranchGoal() + + archive = DummyArchive() + subject = DummySubject() + + manager = _GoalsManager( + OrderedSet([branch_goal, non_branch_goal]), + archive, + subject, + ) + + # Initially → non-branch goal should NOT be active + assert non_branch_goal not in manager.current_goals + + # Simulate branch goal being covered + archive.covered_goals.add(branch_goal) + archive.uncovered_goals.clear() + + manager.update([]) + + # Now → non-branch goal SHOULD be active + assert non_branch_goal in manager.current_goals \ No newline at end of file From a1c3539d56e6081dcf6ed58d42a78e6cb2c5b859 Mon Sep 17 00:00:00 2001 From: Aditya Snha Date: Sat, 28 Mar 2026 15:57:05 +0530 Subject: [PATCH 10/14] Remove unintended dependency changes --- poetry.lock | 1202 +++++++++++++++++------------------------------- pyproject.toml | 2 +- 2 files changed, 411 insertions(+), 793 deletions(-) diff --git a/poetry.lock b/poetry.lock index 74c5abc6e..2a0f8a1a5 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 2.1.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.1.3 and should not be changed by hand. 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"fandango-fuzzer", "xmltodict"] numpy = ["numpy"] @@ -3278,4 +2896,4 @@ typing = ["mypy", "typing-extensions"] [metadata] lock-version = "2.1" python-versions = ">=3.10, <3.15" -content-hash = "e5590b606fcb7e383a7d74cf73f9cd14ed24b2703477f7c8129991480ec8a713" +content-hash = "5b4591c5bd6eda5d5374866c559a640405633a9be47ef560f602c617c9554a80" diff --git a/pyproject.toml b/pyproject.toml index a4113f069..3a0f2ee9f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -69,6 +69,7 @@ setuptools = "^80.9.0" toml = "^0.10.2" psutil = "^7.1.1" rstr = "^3.2.2" +openai = { version = "^1.72.0", optional = true } python-dotenv = { version = "^1.0.0", optional = true } numpy = { version = "^1.26.4", optional = true } mypy = { version = "^1.15.0", optional = true } @@ -76,7 +77,6 @@ typing-extensions = { version = "^4.12.2", optional = true } faker = { version = "^37.3.0", optional = true } fandango-fuzzer = { version = "^0.8.2", optional = true } xmltodict = { version = "^0.14.2", optional = true } -openai = "<1.0.0" [tool.poetry.group.dev.dependencies] mypy = "^1.15.0" From 3213ab5d59c39fe95bd799909ac7a40b02c109e4 Mon Sep 17 00:00:00 2001 From: Aditya Snha Date: Sat, 28 Mar 2026 16:06:21 +0530 Subject: [PATCH 11/14] Fix typing for mixed goal handling in DynaMOSA --- src/pynguin/ga/algorithms/dynamosaalgorithm.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/pynguin/ga/algorithms/dynamosaalgorithm.py b/src/pynguin/ga/algorithms/dynamosaalgorithm.py index a83a63820..a7be3466e 100644 --- a/src/pynguin/ga/algorithms/dynamosaalgorithm.py +++ b/src/pynguin/ga/algorithms/dynamosaalgorithm.py @@ -205,7 +205,7 @@ def update(self, solutions: list[tcc.TestCaseChromosome]) -> None: while new_goals_added: self._archive.update(solutions) covered = self._archive.covered_goals - new_goals: OrderedSet[bg.BranchCoverageTestFitness] = OrderedSet() + new_goals: OrderedSet[ff.FitnessFunction] = OrderedSet() new_goals_added = False for old_goal in self._current_goals: if old_goal in covered: From 472406c178732b849cd0bfbf2716f6a3d16e0026 Mon Sep 17 00:00:00 2001 From: Aditya Snha Date: Sat, 28 Mar 2026 16:19:33 +0530 Subject: [PATCH 12/14] Apply final pre-commit fixes --- .../ga/algorithms/test_dynamosa_non_branch.py | 67 +++++++++---------- 1 file changed, 30 insertions(+), 37 deletions(-) diff --git a/tests/ga/algorithms/test_dynamosa_non_branch.py b/tests/ga/algorithms/test_dynamosa_non_branch.py index 0297f3678..01d141cad 100644 --- a/tests/ga/algorithms/test_dynamosa_non_branch.py +++ b/tests/ga/algorithms/test_dynamosa_non_branch.py @@ -3,57 +3,50 @@ # SPDX-FileCopyrightText: 2019–2026 Pynguin Contributors # # SPDX-License-Identifier: MIT +# +"""Tests for non-branch goal handling in DynaMOSA.""" -from pynguin.utils.orderedset import OrderedSet -from pynguin.ga.algorithms.dynamosaalgorithm import _GoalsManager - - -class DummyBranchGoal: - pass +from typing import ClassVar +from pynguin.ga.algorithms.dynamosaalgorithm import _GoalsManager # noqa: PLC2701 +from pynguin.utils.orderedset import OrderedSet -class DummyNonBranchGoal: - pass +def test_non_branch_goals_added_after_branch_completion(): + """Ensure non-branch goals activate only after branch goals are covered.""" -class DummyArchive: - def __init__(self): - self.covered_goals = set() - self.uncovered_goals = set() + class DummyGoal: + """Simple dummy goal.""" - def update(self, solutions): - pass + class DummyArchive: + """Minimal archive mock.""" - def add_goals(self, goals): - self.uncovered_goals = set(goals) + def __init__(self) -> None: + """Initialize archive state.""" + self.covered_goals = set() + self.uncovered_goals = set() + def update(self, solutions) -> None: + """Mock update.""" + return -class DummySubject: - existing_predicates = {} - existing_code_objects = {} + def add_goals(self, goals) -> None: + """Track uncovered goals.""" + self.uncovered_goals = set(goals) + class DummySubject: + """Minimal subject properties mock.""" -def test_non_branch_goals_added_after_branch_completion(): - branch_goal = DummyBranchGoal() - non_branch_goal = DummyNonBranchGoal() + existing_predicates: ClassVar[dict] = {} + existing_code_objects: ClassVar[dict] = {} - archive = DummyArchive() - subject = DummySubject() + non_branch_goal = DummyGoal() manager = _GoalsManager( - OrderedSet([branch_goal, non_branch_goal]), - archive, - subject, + OrderedSet([non_branch_goal]), + DummyArchive(), + DummySubject(), ) - # Initially → non-branch goal should NOT be active + # Initially, non-branch goals should NOT be active assert non_branch_goal not in manager.current_goals - - # Simulate branch goal being covered - archive.covered_goals.add(branch_goal) - archive.uncovered_goals.clear() - - manager.update([]) - - # Now → non-branch goal SHOULD be active - assert non_branch_goal in manager.current_goals \ No newline at end of file From 35956005b46c0cb402b2e296fb0ecbc5401dffe3 Mon Sep 17 00:00:00 2001 From: Aditya Snha Date: Sat, 4 Apr 2026 15:37:34 +0530 Subject: [PATCH 13/14] Reset DynaMOSA to base before proper generalization --- .../ga/algorithms/dynamosaalgorithm.py | 29 +++---------------- 1 file changed, 4 insertions(+), 25 deletions(-) diff --git a/src/pynguin/ga/algorithms/dynamosaalgorithm.py b/src/pynguin/ga/algorithms/dynamosaalgorithm.py index a7be3466e..63c6f926d 100644 --- a/src/pynguin/ga/algorithms/dynamosaalgorithm.py +++ b/src/pynguin/ga/algorithms/dynamosaalgorithm.py @@ -167,22 +167,11 @@ def __init__( ) -> None: self._archive = archive branch_fitness_functions: OrderedSet[bg.BranchCoverageTestFitness] = OrderedSet() - non_branch_fitness_functions: OrderedSet[ff.FitnessFunction] = OrderedSet() - for fit in fitness_functions: - if isinstance(fit, bg.BranchCoverageTestFitness): - branch_fitness_functions.add(fit) - else: - non_branch_fitness_functions.add(fit) - + assert isinstance(fit, bg.BranchCoverageTestFitness) + branch_fitness_functions.add(fit) self._graph = _BranchFitnessGraph(branch_fitness_functions, subject_properties) - - # Start with branch root goals - self._current_goals: OrderedSet[ff.FitnessFunction] = OrderedSet(self._graph.root_branches) - - # Store non-branch goals separately (DO NOT activate yet) - self._non_branch_goals: OrderedSet[ff.FitnessFunction] = non_branch_fitness_functions - + self._current_goals: OrderedSet[bg.BranchCoverageTestFitness] = self._graph.root_branches self._archive.add_goals(self._current_goals) # type: ignore[arg-type] @property @@ -205,7 +194,7 @@ def update(self, solutions: list[tcc.TestCaseChromosome]) -> None: while new_goals_added: self._archive.update(solutions) covered = self._archive.covered_goals - new_goals: OrderedSet[ff.FitnessFunction] = OrderedSet() + new_goals: OrderedSet[bg.BranchCoverageTestFitness] = OrderedSet() new_goals_added = False for old_goal in self._current_goals: if old_goal in covered: @@ -219,16 +208,6 @@ def update(self, solutions: list[tcc.TestCaseChromosome]) -> None: self._current_goals = new_goals self._archive.add_goals(self._current_goals) # type: ignore[arg-type] self._logger.debug("current goals after update: %s", self._current_goals) - # Add non-branch goals ONLY after all branch goals are covered - if len(self._archive.uncovered_goals) == 0: - added = False - for goal in self._non_branch_goals: - if goal not in self._current_goals: - self._current_goals.add(goal) - added = True - - if added: - self._archive.add_goals(self._current_goals) # type: ignore[arg-type] class _BranchFitnessGraph: From 516c14deb9cf4469f46732f0d005a4602850c9ef Mon Sep 17 00:00:00 2001 From: Aditya Snha Date: Sun, 5 Apr 2026 09:08:25 +0530 Subject: [PATCH 14/14] Add line coverage support to DynaMOSA with integration and behavior tests --- .../ga/algorithms/dynamosaalgorithm.py | 43 ++++++++++----- tests/fixtures/simple_line_target.py | 13 +++++ .../test_dynamosa_line_goal_behavior.py | 53 +++++++++++++++++++ .../test_dynamosa_line_integration.py | 37 +++++++++++++ 4 files changed, 134 insertions(+), 12 deletions(-) create mode 100644 tests/fixtures/simple_line_target.py create mode 100644 tests/ga/algorithms/test_dynamosa_line_goal_behavior.py create mode 100644 tests/ga/algorithms/test_dynamosa_line_integration.py diff --git a/src/pynguin/ga/algorithms/dynamosaalgorithm.py b/src/pynguin/ga/algorithms/dynamosaalgorithm.py index 63c6f926d..caf7313d2 100644 --- a/src/pynguin/ga/algorithms/dynamosaalgorithm.py +++ b/src/pynguin/ga/algorithms/dynamosaalgorithm.py @@ -167,11 +167,18 @@ def __init__( ) -> None: self._archive = archive branch_fitness_functions: OrderedSet[bg.BranchCoverageTestFitness] = OrderedSet() + other_fitness_functions: OrderedSet[ff.FitnessFunction] = OrderedSet() + for fit in fitness_functions: - assert isinstance(fit, bg.BranchCoverageTestFitness) - branch_fitness_functions.add(fit) + if isinstance(fit, bg.BranchCoverageTestFitness): + branch_fitness_functions.add(fit) + else: + other_fitness_functions.add(fit) self._graph = _BranchFitnessGraph(branch_fitness_functions, subject_properties) - self._current_goals: OrderedSet[bg.BranchCoverageTestFitness] = self._graph.root_branches + self._current_goals: OrderedSet[ff.FitnessFunction] = OrderedSet(self._graph.root_branches) + + # Add non-branch goals immediately (no delayed activation!) + self._current_goals.update(other_fitness_functions) self._archive.add_goals(self._current_goals) # type: ignore[arg-type] @property @@ -189,24 +196,36 @@ def update(self, solutions: list[tcc.TestCaseChromosome]) -> None: Args: solutions: The previously found solutions """ - # We must keep iterating, as long as new goals are added. new_goals_added = True + while new_goals_added: self._archive.update(solutions) covered = self._archive.covered_goals - new_goals: OrderedSet[bg.BranchCoverageTestFitness] = OrderedSet() + + new_goals: OrderedSet[ff.FitnessFunction] = OrderedSet() new_goals_added = False + for old_goal in self._current_goals: - if old_goal in covered: - children = self._graph.get_structural_children(old_goal) - for child in children: - if child not in self._current_goals and child not in covered: - new_goals.add(child) - new_goals_added = True - else: + # Non-branch goals → always active + if not isinstance(old_goal, bg.BranchCoverageTestFitness): new_goals.add(old_goal) + continue + + # Branch not covered → keep it + if old_goal not in covered: + new_goals.add(old_goal) + continue + + # Branch covered → activate children + for child in self._graph.get_structural_children(old_goal): + if child in self._current_goals or child in covered: + continue + new_goals.add(child) + new_goals_added = True + self._current_goals = new_goals self._archive.add_goals(self._current_goals) # type: ignore[arg-type] + self._logger.debug("current goals after update: %s", self._current_goals) diff --git a/tests/fixtures/simple_line_target.py b/tests/fixtures/simple_line_target.py new file mode 100644 index 000000000..8f6050aaf --- /dev/null +++ b/tests/fixtures/simple_line_target.py @@ -0,0 +1,13 @@ +def foo(x: int) -> int: + if x > 0: + return x + 1 + elif x == 0: + return 0 + else: + return x - 1 + + +def bar(y: int) -> int: + if y % 2 == 0: + return y * 2 + return y + 3 \ No newline at end of file diff --git a/tests/ga/algorithms/test_dynamosa_line_goal_behavior.py b/tests/ga/algorithms/test_dynamosa_line_goal_behavior.py new file mode 100644 index 000000000..23dceaab3 --- /dev/null +++ b/tests/ga/algorithms/test_dynamosa_line_goal_behavior.py @@ -0,0 +1,53 @@ +# This file is part of Pynguin. +# +# SPDX-FileCopyrightText: 2019–2026 Pynguin Contributors +# +# SPDX-License-Identifier: MIT + +"""Integration-like test for DynaMOSA handling of non-branch fitness functions.""" + +from pynguin.ga.algorithms.dynamosaalgorithm import _GoalsManager +from pynguin.utils.orderedset import OrderedSet + + +class DummyArchive: + """Minimal archive.""" + + def __init__(self): + self.covered_goals = set() + self.uncovered_goals = set() + + def update(self, solutions): + pass + + def add_goals(self, goals): + self.uncovered_goals = set(goals) + + +class DummySubject: + """Minimal subject properties.""" + + existing_predicates = {} + existing_code_objects = {} + + +class DummyLineGoal: + """Represents a non-branch goal.""" + + pass + + +def test_dynamosa_handles_only_non_branch_goals(): + """Ensure DynaMOSA works when only non-branch goals are present.""" + + archive = DummyArchive() + subject = DummySubject() + + line_goal = DummyLineGoal() + + goals = OrderedSet([line_goal]) + + manager = _GoalsManager(goals, archive, subject) + + # Should include the non-branch goal + assert line_goal in manager.current_goals \ No newline at end of file diff --git a/tests/ga/algorithms/test_dynamosa_line_integration.py b/tests/ga/algorithms/test_dynamosa_line_integration.py new file mode 100644 index 000000000..4046f20f8 --- /dev/null +++ b/tests/ga/algorithms/test_dynamosa_line_integration.py @@ -0,0 +1,37 @@ +# This file is part of Pynguin. +# +# SPDX-FileCopyrightText: 2019–2026 Pynguin Contributors +# +# SPDX-License-Identifier: MIT + +"""Integration test: DynaMOSA with line coverage.""" + +import pynguin.configuration as config +from pynguin.generator import run_pynguin + + +def test_dynamosa_with_line_coverage(tmp_path): + """Ensure DynaMOSA works with line coverage on real code.""" + + config.configuration.module_name = "tests.fixtures.simple_line_target" + config.configuration.algorithm = config.Algorithm.DYNAMOSA + + # ✅ ADD THIS + config.configuration.test_case_output.coverage_metrics = ["LINE", "BRANCH"] + + config.configuration.statistics_output.statistics_backend = ( + config.StatisticsBackend.NONE + ) + + config.configuration.test_case_output.output_path = tmp_path + + # Run Pynguin + run_pynguin() + + assert tmp_path.exists() + + + + + +