def do_refactoring(self):
logger.info(f"Running {self.name}")
logger.info(f"Parameters {self.params}")
try:
self.main(**self.params)
except Exception as e:
logger.error(f"Error in executing refactoring:\n {e}")
def _evaluate(self,
x, #
out,
*args,
**kwargs):
"""
This method iterate over an Individual, execute the refactoring operation sequentially,
and compute quality attributes for the refactored version of the program, as objectives of the search
params:
x[0] (Individual): x[0] is an instance of Individual (i.e., a list of refactoring operations)
"""
# Stage 0: Git restore
logger.debug("Executing git restore.")
git_restore(config.PROJECT_PATH)
update_understand_database(config.UDB_PATH)
# Stage 1: Execute all refactoring operations in the sequence x
logger.debug(f"Reached Individual with Size {len(x[0])}")
for refactoring_operation in x[0]:
refactoring_operation.do_refactoring()
# Update Understand DB
update_understand_database(config.UDB_PATH)
# Stage 2: Computing quality attributes
score = testability_main(config.UDB_PATH)
logger.info(f"Testability Score: {score}")
# Stage 3: Marshal objectives into vector
out["F"] = np.array([-1 * score], dtype=float)
def _evaluate(self,
x, #
out,
*args,
**kwargs):
"""
This method iterate over a population, execute the refactoring operations in each individual sequentially,
and compute quality attributes for the refactored version of the program, as objectives of the search
Args:
x (Population): x is a matrix where each row is an individual, and each column a variable. \
We have one variable of type list (Individual) ==> x.shape = (len(Population), 1)
"""
objective_values = []
for k, individual_ in enumerate(x):
# Stage 0: Git restore
logger.debug("Executing git restore.")
git_restore(config.PROJECT_PATH)
logger.debug("Updating understand database after git restore.")
update_understand_database(config.UDB_PATH)
# Stage 1: Execute all refactoring operations in the sequence x
logger.debug(f"Reached Individual with Size {len(individual_[0])}")
for refactoring_operation in individual_[0]:
refactoring_operation.do_refactoring()
# Update Understand DB
logger.debug(f"Updating understand database after {refactoring_operation.name}.")
update_understand_database(config.UDB_PATH)
# Stage 2:
arr = Array('d', range(self.n_obj_virtual))
if self.evaluate_in_parallel:
# Stage 2 (parallel mood): Computing quality attributes
p1 = Process(target=calc_qmood_objectives, args=(arr,))
p2 = Process(target=calc_testability_objective, args=(config.UDB_PATH, arr,))
p3 = Process(target=calc_modularity_objective, args=(config.UDB_PATH, arr,))
p1.start(), p2.start(), p3.start()
p1.join(), p2.join(), p3.join()
o1 = sum([i for i in arr[:6]]) / 6.
o2 = arr[6]
o3 = arr[7]
else:
# Stage 2 (sequential mood): Computing quality attributes
qmood_quality_attributes = DesignQualityAttributes(udb_path=config.UDB_PATH)
o1 = qmood_quality_attributes.average_sum
o2 = testability_main(config.UDB_PATH, initial_value=config.CURRENT_METRICS.get("TEST", 1.0))
o3 = modularity_main(config.UDB_PATH, initial_value=config.CURRENT_METRICS.get("MODULE", 1.0))
del qmood_quality_attributes
# Stage 3: Marshal objectives into vector
objective_values.append([-1 * o1, -1 * o2, -1 * o3])
logger.info(f"Objective values for individual {k}: {[-1 * o1, -1 * o2, -1 * o3]}")
# Stage 4: Marshal all objectives into out dictionary
out['F'] = np.array(objective_values, dtype=float)
def do_refactoring(self):
"""
Check preconditions and apply refactoring operation to source code
Returns:
result (boolean): The result statues of the applied refactoring
"""
logger.info(f"Running {self.name}")
logger.info(f"Parameters {self.params}")
try:
res = self.main(**self.params)
logger.debug(f"Executed refactoring with result {res}")
return res
except Exception as e:
logger.error(f"Unexpected error in executing refactoring:\n {e}")
return False
def main():
# Define search algorithms
algorithms = list()
# 1: GA
algorithm = GA(
pop_size=config.POPULATION_SIZE,
sampling=SudoRandomInitialization(),
# crossover=AdaptiveSinglePointCrossover(prob=0.8),
crossover=get_crossover("real_k_point", n_points=2),
mutation=BitStringMutation(),
eliminate_duplicates=RefactoringSequenceDuplicateElimination()
)
algorithms.append(algorithm)
# 2: NSGA II
algorithm = NSGA2(pop_size=config.POPULATION_SIZE,
sampling=SudoRandomInitialization(),
# crossover=AdaptiveSinglePointCrossover(prob=0.8),
crossover=get_crossover("real_k_point", n_points=2),
mutation=BitStringMutation(),
eliminate_duplicates=RefactoringSequenceDuplicateElimination()
)
algorithms.append(algorithm)
# 3: NSGA III
# Todo: Ask for best practices in determining ref_dirs
ref_dirs = get_reference_directions("energy", 8, 90, seed=1)
algorithm = NSGA3(ref_dirs=ref_dirs,
pop_size=config.POPULATION_SIZE,
sampling=SudoRandomInitialization(),
# crossover=AdaptiveSinglePointCrossover(prob=0.8),
crossover=get_crossover("real_k_point", n_points=2),
mutation=BitStringMutation(),
eliminate_duplicates=RefactoringSequenceDuplicateElimination()
)
algorithms.append(algorithm)
# Define problems
problems = list()
problems.append(
ProblemSingleObjective(n_refactorings_lowerbound=config.LOWER_BAND, n_refactorings_upperbound=config.UPPER_BAND)
)
problems.append(
ProblemMultiObjective(n_refactorings_lowerbound=config.LOWER_BAND, n_refactorings_upperbound=config.UPPER_BAND)
)
problems.append(
ProblemManyObjective(n_refactorings_lowerbound=config.LOWER_BAND, n_refactorings_upperbound=config.UPPER_BAND)
)
# Do optimization for various problems with various algorithms
res = minimize(problem=problems[2],
algorithm=algorithms[2],
termination=('n_gen', config.MAX_ITERATIONS),
seed=1,
verbose=True)
logger.info("** FINISHED **")
logger.info("Best Individual:")
logger.info(res.X)
logger.info("Objective Values:")
logger.info(res.F)
logger.info("==================")
logger.info("Other Solutions:")
for ind in res.opt:
logger.info(ind.X)
logger.info(ind.F)
logger.info("==================")
logger.info(f"Start Time: {res.start_time}")
logger.info(f"End Time: {res.end_time}")
logger.info(f"Execution Time in Seconds: {res.exec_time}")
def _evaluate(self,
x, #
out,
*args,
**kwargs):
"""
This method iterate over an Individual, execute the refactoring operation sequentially,
and compute quality attributes for the refactored version of the program, as objectives of the search
params:
x (Individual): x is an instance of Individual (i.e., a list of refactoring operations)
"""
# Git restore`
logger.debug("Executing git restore.")
git_restore(config.PROJECT_PATH)
update_understand_database(config.UDB_PATH)
# Stage 1: Execute all refactoring operations in the sequence x
logger.debug(f"Reached Individual with Size {len(x[0])}")
for refactoring_operation in x[0]:
refactoring_operation.do_refactoring()
# Update Understand DB
update_understand_database(config.UDB_PATH)
# Stage 2: Computing quality attributes
qmood = DesignQualityAttributes(udb_path=config.UDB_PATH)
o1 = qmood.reusability
o2 = qmood.understandability
o3 = qmood.flexibility
o4 = qmood.functionality
o5 = qmood.effectiveness
o6 = qmood.extendability
del qmood
o7 = testability_main(config.UDB_PATH)
o8 = modularity_main(config.UDB_PATH)
logger.info(f"Reusability Score: {o1}")
logger.info(f"Understandability Score: {o2}")
logger.info(f"Flexibility Score: {o3}")
logger.info(f"Functionality Score: {o4}")
logger.info(f"Effectiveness Score: {o5}")
logger.info(f"Extendability Score: {o6}")
logger.info(f"Testability Score: {o7}")
logger.info(f"Modularity Score: {o8}")
# Stage 3: Marshal objectives into vector
out["F"] = np.array([-1 * o1, -1 * o2, -1 * o3, -1 * o4, -1 * o5, -1 * o6, -1 * o7, -1 * o8, ], dtype=float)
def log_project_info(reset_=True, design_metrics_path=None, quality_attributes_path=None,
generation=0, testability_verbose=True, testability_log_path=None):
"""
Logging project metrics and information
"""
if reset_:
reset_project()
if quality_attributes_path is None:
quality_attributes_path = os.path.join(config.PROJECT_LOG_DIR, 'quality_attrs_initial_values.csv')
if design_metrics_path is None:
design_metrics_path = os.path.join(config.PROJECT_LOG_DIR, 'design_metrics.csv')
design_quality_attribute = DesignQualityAttributes(config.UDB_PATH)
avg_, sum_ = design_quality_attribute.average_sum
predicted_testability = testability_main(
config.UDB_PATH,
initial_value=config.CURRENT_METRICS.get("TEST", 1.0),
verbose=testability_verbose,
log_path=testability_log_path
)
mdg_modularity = modularity_main(
config.UDB_PATH,
initial_value=config.CURRENT_METRICS.get("MODULE", 1.0)
)
design_metrics = {
"DSC": [design_quality_attribute.DSC],
"NOH": [design_quality_attribute.NOH],
"ANA": [design_quality_attribute.ANA],
"MOA": [design_quality_attribute.MOA],
"DAM": [design_quality_attribute.DAM],
"CAMC": [design_quality_attribute.CAMC],
"CIS": [design_quality_attribute.CIS],
"NOM": [design_quality_attribute.NOM],
"DCC": [design_quality_attribute.DCC],
"MFA": [design_quality_attribute.MFA],
"NOP": [design_quality_attribute.NOP]
}
quality_objectives = {
"generation": [generation],
"reusability": [design_quality_attribute.reusability],
"understandability": [design_quality_attribute.understandability],
"flexibility": [design_quality_attribute.flexibility],
"functionality": [design_quality_attribute.functionality],
"effectiveness": [design_quality_attribute.effectiveness],
"extendability": [design_quality_attribute.extendability],
"testability": [predicted_testability],
"modularity": [mdg_modularity],
}
logger.info('QMOOD design metrics (N):')
logger.info(design_metrics)
logger.info('Objectives:')
logger.info(quality_objectives)
logger.info('QMOOD quality attributes sum:')
logger.info(sum_)
logger.info('QMOOD quality attributes mean:')
logger.info(avg_)
df_quality_attributes = pd.DataFrame(data=quality_objectives)
if os.path.exists(quality_attributes_path):
df = pd.read_csv(quality_attributes_path, index_col=False)
df_result = pd.concat([df, df_quality_attributes], ignore_index=True)
df_result.to_csv(quality_attributes_path, index=False)
else:
df_quality_attributes.to_csv(quality_attributes_path, index=False)
df_design_metrics = pd.DataFrame(data=design_metrics)
if os.path.exists(design_metrics_path):
df = pd.read_csv(design_metrics_path, index_col=False)
df_results = pd.concat([df, df_design_metrics], ignore_index=True)
# df = df.append(df_design_metrics, ignore_index=True)
df_results.to_csv(design_metrics_path, index=False)
else:
df_design_metrics.to_csv(design_metrics_path, index=False)
def notify(self, algorithm, **kwargs):
# self.data["best"].append(algorithm.pop.get("F").min())
logger.info(f'Generation #{algorithm.n_gen + config.NGEN} was finished:')
# logger.info(f'Best solution:')
# logger.info(f'{algorithm.pop.get("F")}')
# logger.info(f'Pareto-front solutions:')
# logger.info(f'{algorithm.pf}')
X, F, CV, G = algorithm.opt.get("X", "F", "CV", "G")
logger.info(f'Optimum solutions:')
logger.info(f'{F}')
# Log evolved population at end of each generation
generation_log_path = f'{config.PROJECT_LOG_DIR}generations_logs/'
if not os.path.exists(generation_log_path):
os.makedirs(generation_log_path)
generation_endof_date_time = config.dt.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
population_log_file_path = os.path.join(
generation_log_path,
f'pop_gen{algorithm.n_gen + config.NGEN}_{generation_endof_date_time}.json'
)
pop_opt_log_file_path = os.path.join(
generation_log_path,
f'pop_opt_gen{algorithm.n_gen + config.NGEN}_{generation_endof_date_time}.json'
)
pop_opt_objective_value_log = os.path.join(
config.PROJECT_LOG_DIR,
f'{config.PROJECT_NAME}_objectives_log_{config.global_execution_start_time}.csv'
)
population_trimmed = []
for chromosome in algorithm.pop:
chromosome_new = []
for gene_ in chromosome.X[0]:
chromosome_new.append((gene_.name, gene_.params))
population_trimmed.append(chromosome_new)
with open(population_log_file_path, 'w', encoding='utf-8') as fp:
json.dump(population_trimmed, fp, indent=4)
population_trimmed = []
objective_values_content = ''
for chromosome in algorithm.opt:
chromosome_new = []
for gene_ in chromosome.X[0]:
chromosome_new.append((gene_.name, gene_.params))
population_trimmed.append(chromosome_new)
objective_values_content += f'{algorithm.n_gen + config.NGEN},'
for gene_objective_ in chromosome.F:
objective_values_content += f'{gene_objective_},'
objective_values_content += '\n'
with open(pop_opt_log_file_path, mode='w', encoding='utf-8') as fp:
json.dump(population_trimmed, fp, indent=4)
if not os.path.exists(pop_opt_objective_value_log):
writing_mode = 'w'
else:
writing_mode = 'a'
with open(pop_opt_objective_value_log, mode=writing_mode, encoding='utf-8') as fp:
fp.write(objective_values_content)
logger.info('-' * 100)
logger.info(' ')
def _do(self, problem, X, **kwargs):
"""
For population X
Args:
problem (Problem): An instance of pymoo Problem class to be optimized.
X (np.array): Population
"""
# The input of has the following shape (n_parents, n_matings, n_var)
_, n_matings, n_var = X.shape
# The output will be with the shape (n_offsprings, n_matings, n_var)
# Because there the number of parents and offsprings are equal it keeps the shape of X
Y = np.full_like(X, None, dtype=object)
# print(X.shape)
# print(X)
# for each mating provided
for k in range(n_matings):
# get the first and the second parent (a and b are instance of individuals)
a, b = X[0, k, 0], X[1, k, 0]
# print('### a', a)
# print('### b', b)
# print('len a', len(a))
# print('len b', len(b))
len_min = min(len(a), len(b))
cross_point_1 = random.randint(1, int(len_min * 0.30))
cross_point_2 = random.randint(int(len_min * 0.70), len_min - 1)
if random.random() < 0.5:
cross_point_final = cross_point_1
else:
cross_point_final = cross_point_2
logger.info(f'cross_point_final: {cross_point_final}')
offspring_a = []
offspring_b = []
for i in range(0, cross_point_final):
offspring_a.append(deepcopy(a[i]))
offspring_b.append(deepcopy(b[i]))
for i in range(cross_point_final, len_min):
offspring_a.append(deepcopy(b[i]))
offspring_b.append(deepcopy(a[i]))
if len(b) > len(a):
for i in range(len(a), len(b)):
offspring_a.append(deepcopy(b[i]))
else:
for i in range(len(b), len(a)):
offspring_b.append(deepcopy(a[i]))
# print('$$$ offspring_a', offspring_a)
# print('$$$ offspring_b', offspring_b)
# print('len offspring_a', len(offspring_a))
# print('len offspring_b', len(offspring_b))
# Join offsprings to offspring population Y
Y[0, k, 0], Y[1, k, 0] = offspring_a, offspring_b
# quit()
return Y
def _evaluate(self, x, out, *args, **kwargs):
"""
This method iterate over a population, execute the refactoring operations in each individual sequentially,
and compute quality attributes for the refactored version of the program, as objectives of the search.
By default, elementwise_evaluation is set to False, which implies the _evaluate retrieves a set of solutions.
Args:
x (Population): x is a matrix where each row is an individual, and each column a variable.\
We have one variable of type list (Individual) ==> x.shape = (len(Population), 1)
"""
objective_values = []
for k, individual_ in enumerate(x):
# Stage 0: Git restore
logger.debug("Executing git restore.")
git_restore(config.PROJECT_PATH)
logger.debug("Updating understand database after git restore.")
update_understand_database(config.UDB_PATH)
# Stage 1: Execute all refactoring operations in the sequence x
logger.debug(f"Reached an Individual with size {len(individual_[0])}")
for refactoring_operation in individual_[0]:
res = refactoring_operation.do_refactoring()
# Update Understand DB
logger.debug(f"Updating understand database after {refactoring_operation.name}.")
update_understand_database(config.UDB_PATH)
# Stage 2:
arr = Array('d', range(self.n_obj))
if self.evaluate_in_parallel:
# Stage 2 (parallel mood): Computing quality attributes
p1 = Process(target=calc_qmood_objectives, args=(arr,))
if self.n_obj == 8:
p2 = Process(target=calc_testability_objective, args=(config.UDB_PATH, arr,))
p3 = Process(target=calc_modularity_objective, args=(config.UDB_PATH, arr,))
p1.start(), p2.start(), p3.start()
p1.join(), p2.join(), p3.join()
else:
p1.start()
p1.join()
else:
# Stage 2 (sequential mood): Computing quality attributes
qmood_quality_attributes = DesignQualityAttributes(udb_path=config.UDB_PATH)
arr[0] = qmood_quality_attributes.reusability
arr[1] = qmood_quality_attributes.understandability
arr[2] = qmood_quality_attributes.flexibility
arr[3] = qmood_quality_attributes.functionality
arr[4] = qmood_quality_attributes.effectiveness
arr[5] = qmood_quality_attributes.extendability
if self.n_obj == 8:
arr[6] = testability_main(config.UDB_PATH, initial_value=config.CURRENT_METRICS.get("TEST", 1.0))
arr[7] = modularity_main(config.UDB_PATH, initial_value=config.CURRENT_METRICS.get("MODULE", 1.0))
if self.verbose_design_metrics:
design_metrics = {
"DSC": [qmood_quality_attributes.DSC],
"NOH": [qmood_quality_attributes.NOH],
"ANA": [qmood_quality_attributes.ANA],
"MOA": [qmood_quality_attributes.MOA],
"DAM": [qmood_quality_attributes.DAM],
"CAMC": [qmood_quality_attributes.CAMC],
"CIS": [qmood_quality_attributes.CIS],
"NOM": [qmood_quality_attributes.NOM],
"DCC": [qmood_quality_attributes.DCC],
"MFA": [qmood_quality_attributes.MFA],
"NOP": [qmood_quality_attributes.NOP]
}
self.log_design_metrics(design_metrics)
del qmood_quality_attributes
# Stage 3: Marshal objectives into vector
objective_values.append([-1 * i for i in arr])
logger.info(f"Objective values for individual {k}: {[i for i in arr]}")
# Stage 4: Marshal all objectives into out dictionary
out['F'] = np.array(objective_values, dtype=float)
def main():
"""
Optimization module main driver
"""
# Define initialization objects
initializer_class = SmellInitialization if config.WARM_START else RandomInitialization
initializer_object = initializer_class(
udb_path=config.UDB_PATH,
population_size=config.POPULATION_SIZE,
lower_band=config.LOWER_BAND,
upper_band=config.UPPER_BAND
)
# -------------------------------------------
# Define optimization problems
problems = list() # 0: Genetic (Single), 1: NSGA-II (Multi), 2: NSGA-III (Many) objectives problems
problems.append(
ProblemSingleObjective(
n_objectives=config.NUMBER_OBJECTIVES,
n_refactorings_lowerbound=config.LOWER_BAND,
n_refactorings_upperbound=config.UPPER_BAND,
evaluate_in_parallel=False,
)
)
problems.append(
ProblemMultiObjective(
n_objectives=config.NUMBER_OBJECTIVES,
n_refactorings_lowerbound=config.LOWER_BAND,
n_refactorings_upperbound=config.UPPER_BAND,
evaluate_in_parallel=False,
)
)
problems.append(
ProblemManyObjective(
n_objectives=config.NUMBER_OBJECTIVES,
n_refactorings_lowerbound=config.LOWER_BAND,
n_refactorings_upperbound=config.UPPER_BAND,
evaluate_in_parallel=False,
verbose_design_metrics=True,
)
)
# Define search algorithms
algorithms = list()
# 1: GA
alg1 = GA(
pop_size=config.POPULATION_SIZE,
sampling=PopulationInitialization(initializer_object),
crossover=AdaptiveSinglePointCrossover(prob=config.CROSSOVER_PROBABILITY),
# crossover=get_crossover("real_k_point", n_points=2),
mutation=BitStringMutation(prob=config.MUTATION_PROBABILITY, initializer=initializer_object),
eliminate_duplicates=ElementwiseDuplicateElimination(cmp_func=is_equal_2_refactorings_list),
n_gen=config.NGEN,
)
algorithms.append(alg1)
# 2: NSGA-II
alg2 = NSGA2(
pop_size=config.POPULATION_SIZE,
sampling=PopulationInitialization(initializer_object),
crossover=AdaptiveSinglePointCrossover(prob=config.CROSSOVER_PROBABILITY),
# crossover=get_crossover("real_k_point", n_points=2),
mutation=BitStringMutation(prob=config.MUTATION_PROBABILITY, initializer=initializer_object),
eliminate_duplicates=ElementwiseDuplicateElimination(cmp_func=is_equal_2_refactorings_list),
n_gen=config.NGEN,
)
algorithms.append(alg2)
# 3: NSGA-III
# pop_size must be equal or larger than the number of reference directions
number_of_references_points = config.POPULATION_SIZE - int(config.POPULATION_SIZE * 0.20)
ref_dirs = get_reference_directions(
'energy', # algorithm
config.NUMBER_OBJECTIVES, # number of objectives
number_of_references_points, # number of reference directions
seed=1
)
alg3 = NSGA3(
ref_dirs=ref_dirs,
pop_size=config.POPULATION_SIZE, # 200
sampling=PopulationInitialization(initializer_object),
selection=TournamentSelection(func_comp=binary_tournament),
crossover=AdaptiveSinglePointCrossover(prob=config.CROSSOVER_PROBABILITY, ),
# crossover=get_crossover("real_k_point", n_points=2),
mutation=BitStringMutation(prob=config.MUTATION_PROBABILITY, initializer=initializer_object),
eliminate_duplicates=ElementwiseDuplicateElimination(cmp_func=is_equal_2_refactorings_list),
n_gen=config.NGEN,
)
algorithms.append(alg3)
# Termination of algorithms
my_termination = MultiObjectiveDefaultTermination(
x_tol=None,
cv_tol=None,
f_tol=0.0015,
nth_gen=5,
n_last=5,
n_max_gen=config.MAX_ITERATIONS, # about 1000 - 1400
n_max_evals=1e6
)
# Do optimization for various problems with various algorithms
res = minimize(
problem=problems[config.PROBLEM],
algorithm=algorithms[config.PROBLEM],
termination=my_termination,
seed=1,
verbose=False,
copy_algorithm=True,
copy_termination=True,
save_history=False,
callback=LogCallback(),
)
# np.save('checkpoint', res.algorithm)
# Log results
logger.info(f"***** Algorithm was finished in {res.algorithm.n_gen + config.NGEN} generations *****")
logger.info(" ")
logger.info("============ time information ============")
logger.info(f"Start time: {datetime.fromtimestamp(res.start_time).strftime('%Y-%m-%d %H:%M:%S')}")
logger.info(f"End time: {datetime.fromtimestamp(res.end_time).strftime('%Y-%m-%d %H:%M:%S')}")
logger.info(f"Execution time in seconds: {res.exec_time}")
logger.info(f"Execution time in minutes: {res.exec_time / 60}")
logger.info(f"Execution time in hours: {res.exec_time / (60 * 60)}")
# logger.info(f"Number of generations: {res.algorithm.n_gen}")
# logger.info(f"Number of generations", res.algorithm.termination)
# Log optimum solutions
logger.info("============ All opt solutions ============")
for i, ind in enumerate(res.opt):
logger.info(f'Opt refactoring sequence {i}:')
logger.info(ind.X)
logger.info(f'Opt refactoring sequence corresponding objectives vector {i}:')
logger.info(ind.F)
logger.info("-" * 75)
# Log best refactorings
logger.info("============ Best refactoring sequences (a set of non-dominated solutions) ============")
for i, ind in enumerate(res.X):
logger.info(f'Best refactoring sequence {i}:')
logger.info(ind)
logger.info("-" * 75)
logger.info("============ Best objective values (a set of non-dominated solutions) ============")
for i, ind_objective in enumerate(res.F):
logger.info(f'Best refactoring sequence corresponding objectives vector {i}:')
logger.info(ind_objective)
logger.info("-" * 75)
# Save best refactorings
population_trimmed = []
objective_values_content = ''
for chromosome in res.X:
chromosome_new = []
if config.PROBLEM == 0: # i.e., single objective problem
for gene_ in chromosome:
chromosome_new.append((gene_.name, gene_.params))
else:
for gene_ in chromosome[0]:
chromosome_new.append((gene_.name, gene_.params))
population_trimmed.append(chromosome_new)
for objective_vector in res.F:
objective_values_content += f'{res.algorithm.n_gen + config.NGEN},'
if config.PROBLEM == 0:
objective_values_content += f'{objective_vector},'
else:
for objective_ in objective_vector:
objective_values_content += f'{objective_},'
objective_values_content += '\n'
best_refactoring_sequences_path = os.path.join(
config.PROJECT_LOG_DIR,
f'best_refactoring_sequences_after_{res.algorithm.n_gen + config.NGEN}gens.json'
)
with open(best_refactoring_sequences_path, mode='w', encoding='utf-8') as fp:
json.dump(population_trimmed, fp, indent=4)
best_refactoring_sequences_objectives_path = os.path.join(
config.PROJECT_LOG_DIR,
f'best_refactoring_sequences_objectives_after_{res.algorithm.n_gen + config.NGEN}gens.csv'
)
with open(best_refactoring_sequences_objectives_path, mode='w', encoding='utf-8') as fp:
fp.write(objective_values_content)
try:
pf = res.F
# dm = HighTradeoffPoints()
dm = get_decision_making("high-tradeoff")
I = dm.do(pf)
logger.info("============ High-tradeoff points refactoring sequences ============")
for i, ind in enumerate(res.X[I]):
logger.info(f'High tradeoff points refactoring sequence {i}:')
logger.info(ind)
logger.info("-" * 75)
logger.info("============ High-tradeoff points objective values ============")
for i, ind_objective in enumerate(pf[I]):
logger.info(f'High-tradeoff points refactoring sequence corresponding objectives vector {i}:')
logger.info(ind_objective)
logger.info("-" * 75)
logger.info("High-tradeoff points mean:")
logger.info(np.mean(pf[I], axis=0))
logger.info("High-tradeoff points median:")
logger.info(np.median(pf[I], axis=0))
# Save high-tradeoff refactorings
population_trimmed = []
objective_values_content = ''
for chromosome in res.X[I]:
chromosome_new = []
if config.PROBLEM == 0: # i.e., single objective problem
for gene_ in chromosome:
chromosome_new.append((gene_.name, gene_.params))
else:
for gene_ in chromosome[0]:
chromosome_new.append((gene_.name, gene_.params))
population_trimmed.append(chromosome_new)
for objective_vector in pf[I]:
objective_values_content += f'{res.algorithm.n_gen + config.NGEN},'
if config.PROBLEM == 0:
objective_values_content += f'{objective_vector},'
else:
for objective_ in objective_vector:
objective_values_content += f'{objective_},'
objective_values_content += '\n'
high_tradeoff_path = os.path.join(
config.PROJECT_LOG_DIR,
f'high_tradeoff_points_refactoring_after_{res.algorithm.n_gen + config.NGEN}gens.json'
)
with open(high_tradeoff_path, mode='w', encoding='utf-8') as fp:
json.dump(population_trimmed, fp, indent=4)
high_tradeoff_path_objectives_path = os.path.join(
config.PROJECT_LOG_DIR,
f'high_tradeoff_points_after_{res.algorithm.n_gen + config.NGEN}gens.csv'
)
with open(high_tradeoff_path_objectives_path, mode='w', encoding='utf-8') as fp:
fp.write(objective_values_content)
except:
logger.error("No multi-optimal solutions (error in computing high tradeoff points)!")
for objective_ in objective_vector:
objective_values_content += f'{objective_},'
objective_values_content += '\n'
high_tradeoff_path = os.path.join(
config.PROJECT_LOG_DIR,
f'high_tradeoff_points_refactoring_after_{res.algorithm.n_gen + config.NGEN}gens.json'
)
with open(high_tradeoff_path, mode='w', encoding='utf-8') as fp:
json.dump(population_trimmed, fp, indent=4)
high_tradeoff_path_objectives_path = os.path.join(
config.PROJECT_LOG_DIR,
f'high_tradeoff_points_after_{res.algorithm.n_gen + config.NGEN}gens.csv'
)
with open(high_tradeoff_path_objectives_path, mode='w', encoding='utf-8') as fp:
fp.write(objective_values_content)
except:
logger.error("No multi-optimal solutions (error in computing high tradeoff points)!")
# CodART search-based refactoring module main driver
if __name__ == '__main__':
# print(logger.handlers)
config.log_experiment_info()
logger.info('============ Objectives values before Refactoring ============')
log_project_info(reset_=True)
# quit()
main()
def main(source_class: str, source_package: str, target_class: str, target_package: str, field_name: str,
udb_path: str, *args, **kwargs):
"""
Move filed main API
"""
import_statement = None
if source_package != target_package:
import_statement = f"\nimport {target_package}.{target_class};"
instance_name = target_class.lower() + "ByCodArt"
db = und.open(udb_path)
# Check if field is static
field_ent = db.lookup(f"{source_package}.{source_class}.{field_name}", "Variable")
if len(field_ent) == 0:
logger.error(f"Entity not found with query: {source_package}.{source_class}.{field_name}.")
db.close()
return False
if source_package == target_package and source_class == target_class:
logger.error("Can not move to self.")
db.close()
return False
field_ent = field_ent[0]
is_static = field_ent.kindname() == STATIC
if is_static:
logger.warning("Field is static!")
# Find usages
usages = {}
for ref in field_ent.refs("Setby, Useby"):
file = ref.file().longname()
if file in usages:
usages[file].append(ref.line())
else:
usages[file] = [ref.line(), ]
try:
src_class_file = db.lookup(f"{source_package}.{source_class}.java")[0].longname()
target_class_file = db.lookup(f"{target_package}.{target_class}.java")[0].longname()
except IndexError:
logger.error("This is a nested class.")
logger.info(f"{source_package}.{source_class}.java")
logger.info(f"{target_package}.{target_class}.java")
db.close()
return False
db.close()
# Check if there is an cycle
listener = parse_and_walk(
file_path=target_class_file,
listener_class=CheckCycleListener,
class_name=source_class,
)
if not listener.is_valid:
logger.error(f"Can not move field because there is a cycle between {source_class}, {target_class}")
# db.close()
return False
# Propagate Changes
for file in usages.keys():
parse_and_walk(
file_path=file,
listener_class=PropagateListener,
has_write=True,
field_name=field_name,
new_name=f"{instance_name}.{field_name}",
lines=usages[file],
)
# Do the cut and paste!
# Cut
listener = parse_and_walk(
file_path=src_class_file,
listener_class=CutFieldListener,
has_write=True,
class_name=target_class,
instance_name=instance_name,
field_name=field_name,
is_static=is_static,
import_statement=import_statement
)
field_text = listener.field_text
# Paste
parse_and_walk(
file_path=target_class_file,
listener_class=PasteFieldListener,
has_write=True,
field_text=field_text,
)
# db.close()
return True
def main(source_class: str, source_package: str, target_class: str,
target_package: str, method_name: str, udb_path: str, *args,
**kwargs):
"""
"""
import_statement = None
if source_package != target_package:
import_statement = f"\nimport {target_package}.{target_class};"
instance_name = target_class.lower() + "ByCodArt"
db = und.open(udb_path)
method_map, class_ent = get_source_class_map(db, source_class)
if class_ent is None:
logger.error("Class entity is None")
return False
# Strong overlay precondition
# if class_ent.refs("Extend ~Implicit, ExtendBy, Implement"):
# logger.error("Class is in inheritance or implements an interface.")
# db.close()
# return False
# Check if method is static
method_ent = db.lookup(f"{source_package}.{source_class}.{method_name}",
"Method")
if len(method_ent) >= 1:
method_ent = method_ent[0]
else:
logger.error("Entity not found.")
db.close()
return False
if method_ent.simplename() != method_name:
logger.error("Can not move method duo to duplicated entities.")
logger.info(f"{method_ent}, {method_ent.kindname()}")
db.close()
return False
if source_package == target_package and source_class == target_class:
logger.error("Can not move to self.")
db.close()
return False
is_static = STATIC in method_ent.kindname()
# Find usages
usages = {}
for ref in method_ent.refs("Callby"):
file = ref.file().longname()
if file in usages:
usages[file].append(ref.line())
else:
usages[file] = [
ref.line(),
]
try:
src_class_file = db.lookup(f"{source_package}.{source_class}.java",
"File")[0].longname()
target_class_file = db.lookup(f"{target_package}.{target_class}.java",
"File")[0].longname()
except IndexError:
logger.error("This is a nested method.")
logger.info(f"{source_package}.{source_class}.java")
logger.info(f"{target_package}.{target_class}.java")
db.close()
return False
db.close()
# Check if there is an cycle
listener = parse_and_walk(file_path=target_class_file,
listener_class=CheckCycleListener,
class_name=source_class)
if not listener.is_valid:
logger.error(
f"Can not move method because there is a cycle between {source_class}, {target_class}"
)
# db.close()
return False
# Propagate Changes
for file in usages.keys():
public_class_name = os.path.basename(file).split(".")[0]
is_in_target_class = public_class_name == target_class
parse_and_walk(
file_path=file,
listener_class=PropagateListener,
has_write=True,
method_name=method_name,
new_name=f"{instance_name}.{method_name}",
lines=usages[file],
is_in_target_class=is_in_target_class,
method_map=method_map,
)
# exit(-1)
# Do the cut and paste!
# Cut
listener = parse_and_walk(
file_path=src_class_file,
listener_class=CutMethodListener,
has_write=True,
class_name=target_class,
instance_name=instance_name,
method_name=method_name,
is_static=is_static,
import_statement=import_statement,
)
method_text = listener.method_text
# Paste
listener = parse_and_walk(
file_path=target_class_file,
listener_class=PasteMethodListener,
has_write=True,
method_text=method_text,
source_class=source_class,
method_map=method_map,
imports=listener.imports,
)
# Post-Paste: Reference Injection
parse_and_walk(
file_path=target_class_file,
listener_class=ReferenceInjectorAndConstructorListener,
has_write=True,
method_text=method_text,
source_class=source_class,
method_map=method_map,
imports=None,
has_empty_cons=listener.has_empty_cons,
)
# db.close()
return True
