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calculate_buglocator_features.py
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calculate_buglocator_features.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""Usage: %(scriptName) <bug_report_file> <data_prefix>
"""
import json
from timeit import default_timer
import datetime
import numpy as np
import sys
from multiprocessing import Pool
from operator import itemgetter
from scipy import sparse
from sklearn.metrics.pairwise import cosine_similarity
from tqdm import tqdm
def main():
print("Start", datetime.datetime.now().isoformat())
before = default_timer()
bug_report_file_path = sys.argv[1]
print("bug report file path", bug_report_file_path)
data_prefix = sys.argv[2]
print("data prefix", data_prefix)
bug_reports = load_bug_reports(bug_report_file_path)
process(bug_reports, data_prefix, bug_report_file_path)
after = default_timer()
total = after - before
print("End", datetime.datetime.now().isoformat())
print("total time", total)
def load_bug_reports(bug_report_file_path):
"""load bug report file (the one generated from xml)"""
with open(bug_report_file_path) as bug_report_file:
bug_reports = json.load(bug_report_file)
return bug_reports
def sort_bug_reports_by_id(bug_reports):
dates = []
for index, id in enumerate(tqdm(bug_reports)):
date = bug_reports[id]['bug_report']['timestamp']
dates.append((id, date))
sorted_dates = sorted(dates, key=itemgetter(1))
sorted_ids = [sorted_date[0] for sorted_date in sorted_dates]
return sorted_ids
def load_bug_report(vectorized_data, bug_report_indexes, bug_report_id):
# index_dict = pickle.loads(bug_report_indexes[bug_report_id[0:7]])
index_dict = bug_report_indexes[bug_report_id]
report_index = index_dict['report']
vectorized_report = vectorized_data[report_index, :]
summary_index = index_dict['summary']
vectorized_summary = vectorized_data[summary_index, :]
description_index = index_dict['description']
vectorized_description = vectorized_data[description_index, :]
return vectorized_report, vectorized_summary, vectorized_description
def feature_1(report, data, source_index, method_start_index, method_end_index):
sources = data[source_index:method_end_index+1, :]
similarities = cosine_similarity(report, sources)
return np.amax(similarities)
def feature_2(report, data, enriched_api_indexes, current_file_index):
file_enriched_api = enriched_api_indexes[current_file_index]
enriched_api_start = file_enriched_api['enrichedApiStart']
enriched_api_end = file_enriched_api['enrichedApiEnd']
return feature_sim(report, data, enriched_api_start, enriched_api_end)
def feature_sim(document, data, start_index, end_index):
if start_index == end_index + 1:
return 0.0
sources = data[start_index:end_index+1, :]
similarities = cosine_similarity(document, sources)
return np.amax(similarities)
def feature_3(feature_3_data, feature_3_report_lookup, file_index):
report_index = feature_3_report_lookup['report']
packages_with_directory = file_index.replace('/', '.')
for file in feature_3_report_lookup['files']:
if packages_with_directory.endswith(file):
feature_3_file_index = feature_3_report_lookup['files'][file]
report_row = feature_3_data[report_index, :]
file_row = feature_3_data[feature_3_file_index, :]
return float(cosine_similarity(report_row, file_row))
return 0.0
def feature_4(bug_report_summary, file_index):
class_name = file_index.split('/')[-1].split('.')[0]
if class_name in bug_report_summary:
return len(class_name)
else:
return 0.0
def process(bug_reports, data_prefix, bug_report_file_path):
sorted_ids = sort_bug_reports_by_id(bug_reports)
work = []
for bug_report_id in sorted_ids:
work.append((data_prefix, bug_report_id, bug_report_file_path))
# _f(work[0])
# exit(0)
pool = Pool(12, maxtasksperchild=1)
r = list(tqdm(pool.imap(_f, work), total=len(work)))
print("r", len(r))
def _f(args):
return process_bug_report(args[0], args[1], args[2])
def process_bug_report(data_prefix, bug_report_id, bug_report_file_path):
bug_reports = load_bug_reports(bug_report_file_path)
bug_report = bug_reports[bug_report_id]
vectorized_data = sparse.load_npz(data_prefix+'_raw_count_data.npz')
with open(data_prefix+'_feature_names_dict', 'r') as infile:
feature_names_lenghts_dict = json.load(infile)
with open(data_prefix+'_file_index_lookup', 'r') as infile:
file_index_lookup = json.load(infile)
with open(data_prefix+'_bug_report_index_lookup', 'r') as infile:
bug_report_index_lookup = json.load(infile)
enriched_api_data = sparse.load_npz(data_prefix+'_tfidf_enriched_api.npz').tocsr()
with open(data_prefix + '_partial_enriched_api_index_lookup') as infile:
enriched_api_lookup = json.load(infile)
with open(data_prefix + '_partial_enriched_api_bug_report_index_lookup') as infile:
enriched_api_bug_reports_lookup = json.load(infile)
graph_data = sparse.load_npz(data_prefix+'_graph_features_data.npz').tocsr()
with open(data_prefix + '_graph_features_index_lookup', 'r') as infile:
graph_lookup = json.load(infile)
feature_3_data = sparse.load_npz(data_prefix + '_feature_3_data.npz')
with open(data_prefix + '_feature_3_report_lookup', 'r') as infile:
feature_3_report_lookup = json.load(infile)
with open(data_prefix + '_feature_5_report_lookup', 'r') as infile:
recency_lookup = json.load(infile)
with open(data_prefix + '_feature_6_report_lookup', 'r') as infile:
frequency_lookup = json.load(infile)
(vectorized_report, vectorized_summary, vectorized_description) = load_bug_report(vectorized_data, bug_report_index_lookup, bug_report_id)
enriched_report = enriched_api_data[enriched_api_bug_reports_lookup[bug_report_id], :]
current_feature_3_report_lookup = feature_3_report_lookup[bug_report_id]
current_bug_report_summary = bug_reports[bug_report_id]['bug_report']['summary']
features = []
features_files = []
for file_index in file_index_lookup:
current_lookup = file_index_lookup[file_index]
source_index = current_lookup['source']
method_source_start_index = current_lookup['methodsStart']
method_source_end_index = current_lookup['methodsEnd']
class_start_index = current_lookup['classNamesStart']
class_end_index = current_lookup['classNamesEnd']
method_names_start_index = current_lookup['methodNamesStart']
method_names_end_index = current_lookup['methodNamesEnd']
variable_start_index = current_lookup['variableNamesStart']
variable_end_index = current_lookup['variableNamesEnd']
comment_start_index = current_lookup['commentsStart']
comment_end_index = current_lookup['commentsEnd']
current_graph_lookup = graph_lookup[file_index]
current_recency_lookup = recency_lookup[bug_report_id]
current_frequency_lookup = frequency_lookup[bug_report_id]
class_with_packages_and_directory = file_index.replace('/', '.')
f1 = feature_1(vectorized_report, vectorized_data, source_index, method_source_start_index, method_source_end_index)
f2 = feature_2(enriched_report, enriched_api_data, enriched_api_lookup, file_index)
f3 = feature_3(feature_3_data, current_feature_3_report_lookup, file_index)
f4 = feature_4(current_bug_report_summary, file_index)
f5 = 0.0
for recency_file in current_recency_lookup.keys():
if class_with_packages_and_directory.endswith(recency_file):
f5 = current_recency_lookup[recency_file]
break
f6 = 0.0
for frequency_file in current_frequency_lookup.keys():
if class_with_packages_and_directory.endswith(frequency_file):
f6 = current_recency_lookup[frequency_file]
break
f7 = feature_sim(vectorized_summary, vectorized_data, class_start_index, class_end_index)
f8 = feature_sim(vectorized_summary, vectorized_data, method_names_start_index, method_names_end_index)
f9 = feature_sim(vectorized_summary, vectorized_data, variable_start_index, variable_end_index)
f10 = feature_sim(vectorized_summary, vectorized_data, comment_start_index, comment_end_index)
f11 = feature_sim(vectorized_description, vectorized_data, class_start_index, class_end_index)
f12 = feature_sim(vectorized_description, vectorized_data, method_names_start_index, method_names_end_index)
f13 = feature_sim(vectorized_description, vectorized_data, variable_start_index, variable_end_index)
f14 = feature_sim(vectorized_description, vectorized_data, comment_start_index, comment_end_index)
f15 = graph_data[current_graph_lookup, 0]
f16 = graph_data[current_graph_lookup, 1]
f17 = graph_data[current_graph_lookup, 2]
f18 = graph_data[current_graph_lookup, 3]
f19 = graph_data[current_graph_lookup, 4]
used_in_fix = 0.0
for result in bug_report['bug_report']['result']:
if class_with_packages_and_directory.endswith(result):
used_in_fix = 1.0
break
features.append([f1, f2, f3, f4, f5, f6, f7, f8, f9, f10, f11, f12, f13, f14, f15, f16, f17, f18, f19, used_in_fix])
features_files.append(file_index)
sparse_features = sparse.csr_matrix(features)
sparse.save_npz(data_prefix+'_'+bug_report_id+'_features', sparse_features)
with open(data_prefix+'_'+bug_report_id+'_files', 'w') as outfile:
json.dump(features_files, outfile)
if __name__ == '__main__':
main()