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)
"""
# 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
obj = DesignQualityAttributes(udb_path=config.UDB_PATH)
o1 = obj.average_sum
del obj
o2 = testability_main(config.UDB_PATH)
o3 = modularity_main(config.UDB_PATH)
logger.info(f"QMOOD AVG Score: {o1}")
logger.info(f"Testability Score: {o2}")
logger.info(f"Modularity Score: {o3}")
# Stage 3: Marshal objectives into vector
out["F"] = np.array([-1 * o1, -1 * o2, -1 * o3], 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 _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 _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 calc_modularity_objective(path_, arr_):
arr_[7] = modularity_main(
path_,
initial_value=config.CURRENT_METRICS.get("MODULE", 1.0)
)