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 execute_from_txt_log(self, input_file_path):
"""
input_file_path: The path of input data from log.
Example: Take a look at ./input.txt
"""
with open(input_file_path, 'r') as f:
data = f.read().split('\n')
for row in data:
refactoring_name = re.search(', (.+?)(\w+)*\(',
row).group()[1:-1].strip()
params = re.search('{(.+?)}', row).group().strip()
params = params.replace("'", '"')
params = params.replace("False", "false")
params = params.replace("True", "true")
params = json.loads(params)
if params.get('udb_path'):
params['udb_path'] = config.UDB_PATH
if params.get('project_dir'):
params['project_dir'] = config.PROJECT_PATH
if params.get('file_path'):
params['file_path'] = params['file_path'].replace(
self.file_path_base_dir_old, config.PROJECT_ROOT_DIR)
print(refactoring_name, params)
res = REFACTORING_MAIN_MAP[refactoring_name](**params)
print(f"Executed {refactoring_name} with status {res}")
update_understand_database(config.UDB_PATH)
print('-' * 100)
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)
)
# 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
# Tests
if __name__ == '__main__':
from codart.utility.directory_utils import update_understand_database
update_understand_database("C:/Users/Administrator/Downloads/udbs/jvlt-1.3.2.udb")
main(
source_class="XMLFormatter",
source_package="net.sourceforge.jvlt.io",
target_class="Entry",
target_package="net.sourceforge.jvlt.core",
method_name="getXMLForEntryInfo",
udb_path="C:/Users/Administrator/Downloads/udbs/jvlt-1.3.2.udb"
)
def execute_from_json_log(self, input_file_path=None, reset=True):
if input_file_path is None:
input_file_path = glob.glob(
os.path.join(self.log_directory,
'best_refactoring_sequences*.json'))[0]
# log_project_info(reset_=True, )
population = []
with open(input_file_path, 'r', encoding='utf-8') as fp:
population_trimmed = json.load(fp)
for chromosome in population_trimmed:
chromosome_new = []
for gene_ in chromosome:
refactoring_params = gene_[1]
if refactoring_params.get('udb_path'):
refactoring_params['udb_path'] = config.UDB_PATH
if refactoring_params.get('project_dir'):
refactoring_params['project_dir'] = config.PROJECT_PATH
if refactoring_params.get('file_path'):
refactoring_params['file_path'] = refactoring_params[
'file_path'].replace(self.file_path_base_dir_old,
config.PROJECT_ROOT_DIR)
if refactoring_params.get('class_path'):
refactoring_params['class_path'] = refactoring_params[
'class_path'].replace(self.file_path_base_dir_old,
config.PROJECT_ROOT_DIR)
chromosome_new.append((REFACTORING_MAIN_MAP[gene_[0]],
refactoring_params, gene_[0]))
population.append(chromosome_new)
# print(population)
# quit()
applicability_map = {
'Project': [],
'Sequence': [],
'Applied refactorings': [],
'Rejected refactorings': []
}
for k, refactoring_sequence in enumerate(population):
true_refactorings_count = 0
false_refactorings_count = 0
reset_project()
# Apply sequence X to system S
for refactoring_operation in refactoring_sequence:
res = refactoring_operation[0](**refactoring_operation[1])
update_understand_database(config.UDB_PATH)
if res:
true_refactorings_count += 1
else:
false_refactorings_count += 1
config.logger.info(
f"Executed {refactoring_operation[2]} with status {res}")
applicability_map['Project'].append(config.PROJECT_NAME)
applicability_map['Sequence'].append(k)
applicability_map['Applied refactorings'].append(
true_refactorings_count)
applicability_map['Rejected refactorings'].append(
false_refactorings_count)
continue
# Dump refactored project
dump_path = os.path.join(
config.PROJECT_ROOT_DIR,
f'{config.PROJECT_NAME}_refactored_with_algorithm{config.PROBLEM}_rand',
f'dump{k}')
if not os.path.exists(config.PROJECT_ROOT_DIR):
os.mkdir(dump_path)
shutil.copytree(config.PROJECT_PATH, dump_path)
# Compute quality metrics
log_project_info(
reset_=False,
quality_attributes_path=os.path.join(
config.PROJECT_LOG_DIR,
'best_refactoring_sequences_objectives_extended.csv'),
generation='-1', # config.MAX_ITERATIONS,
testability_verbose=True,
testability_log_path=os.path.join(
config.PROJECT_LOG_DIR,
f'classes_testability2_for_problem_{config.PROBLEM}_best_sequence_{k}.csv'
))
# Log applied and rejected refactorings
df = pd.DataFrame(data=applicability_map)
df.to_csv(os.path.join(config.PROJECT_LOG_DIR,
'applicability_map.csv'),
index=False)
if reset:
reset_project()
]
all_metrics = []
for metric in attrs:
cache = getattr(self, f'_{metric}')
if cache is None:
all_metrics.append(getattr(self, metric))
else:
all_metrics.append(cache)
return round(sum(all_metrics) / len(all_metrics),
5), round(sum(all_metrics), 5)
if __name__ == '__main__':
from codart.utility.directory_utils import update_understand_database
update_understand_database(config.UDB_PATH)
print(f"UDB path: {config.UDB_PATH}")
design_quality_attribute = DesignQualityAttributes(config.UDB_PATH)
metrics_dict = {
"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
# Random search for improving reusability
while k < MAX_ITERATIONS and limit > 0:
print("Iteration No.", k)
index = random.randint(0, limit - 1)
limit -= 1
print(f"index is {index}")
individual = rand_pop.pop(index)
print(f"len(individual) is {len(individual)}")
# git restore prev changes
git_restore(project_dir)
# execute individual
for refactoring, params in individual:
print(params)
try:
refactoring(**params)
except Exception as e:
print(e)
# update understand database
update_understand_database(udb_path)
# calculate objective
obj_score = DesignQualityAttributes(udb_path=udb_path).reusability
print("Objective Score", obj_score)
if obj_score < score:
score = obj_score
best_answer = individual
k += 1
print("=" * 75)
print("Best Score", score)
print("Best Answer", best_answer)
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)