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calculate_vectorized_features.py
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calculate_vectorized_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 pickle
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
from unqlite import UnQLite
from date_utils import convert_commit_date
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 load_vectorized_data(data_prefix):
file_path = data_prefix + '_all_data.npz'
print("vectorized data file path", file_path)
vectorized_data = sparse.load_npz(file_path).tocsr()
print("vectorized data shape", vectorized_data.shape)
return vectorized_data
def sort_bug_reports_by_commit_date(bug_reports):
commit_dates = []
for index, commit in enumerate(tqdm(bug_reports)):
sha = bug_reports[commit]['commit']['metadata']['sha'].replace('commit ', '').strip()
commit_date = convert_commit_date(
bug_reports[commit]['commit']['metadata']['date'].replace('Date:', '').strip())
commit_dates.append((sha, commit_date))
sorted_commit_dates = sorted(commit_dates, key=itemgetter(1))
sorted_commits = [commit_date[0] for commit_date in sorted_commit_dates]
return sorted_commits
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_sha):
file_enriched_api = enriched_api_indexes[current_file_sha]
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(data, lookup, sha):
if sha not in lookup:
return 0.0
file_index = lookup[sha]
report_summary_index = data.shape[0] - 1
return cosine_similarity(data[report_summary_index, :], data[file_index, :])[0][0]
def feature_4(current_bug_report_summary, ast_cache_collection, sha):
class_names = pickle.loads(ast_cache_collection[sha])['classNames']
class_names = remove_package_names(class_names)
lengths = [0]
for class_name in class_names:
if class_name in current_bug_report_summary:
lengths.append(len(class_name))
max_length = max(lengths)
return max_length
def remove_package_names(class_names):
return map(lambda x: remove_package_name(x), class_names)
def remove_package_name(class_name):
return class_name.split(".")[-1]
def process(bug_reports, data_prefix, bug_report_file_path):
sorted_commits = sort_bug_reports_by_commit_date(bug_reports)
with open(data_prefix + '_features_5_6_max', 'r') as maxfile:
features_5_6_max = json.load(maxfile)
max_frequency = features_5_6_max['max_frequency']
work = []
for fixing_commit in sorted_commits:
work.append((data_prefix, fixing_commit, bug_report_file_path, max_frequency))
pool = Pool(12, maxtasksperchild=1)
r = list(tqdm(pool.imap(_f, work), total=len(work)))
print("r", len(r))
# _f(work[0])
def _f(args):
return process_bug_report(args[0], args[1], args[2], args[3])
def retrieve_summary(bug_reports, bug_report_full_sha):
i = 7
partial_key = bug_report_full_sha[0:i]
while partial_key not in bug_reports or i == len(bug_report_full_sha):
i += 1
partial_key = bug_report_full_sha[0:i]
return bug_reports[partial_key]
def process_bug_report(data_prefix, bug_report_full_sha, bug_report_file_path, max_frequency):
bug_report_files_collection_db = UnQLite(data_prefix + "_bug_report_files_collection_db",
flags=0x00000100 | 0x00000001)
current_files = pickle.loads(bug_report_files_collection_db[bug_report_full_sha])
bug_report_files_collection_db.close()
shas = current_files['shas']
sha_to_file_name = current_files['sha_to_file_name']
bug_report_id = bug_report_full_sha[0:7]
vectorized_data = sparse.load_npz(data_prefix + '_' + bug_report_id + '_tf_idf_data.npz')
with open(data_prefix + '_' + bug_report_id + '_tf_idf_index_lookup', 'r') as index_lookup_file:
lookups = json.load(index_lookup_file)
enriched_api_data, enriched_api_indexes = load_enriched_api(data_prefix, bug_report_id)
enriched_report = enriched_api_data[-1, :]
(vectorized_report, vectorized_summary, vectorized_description) = load_bug_report(vectorized_data, lookups,
bug_report_id)
ast_cache_collection = UnQLite(data_prefix + "_ast_cache_collection_db", flags=0x00000100 | 0x00000001)
bug_reports = load_bug_reports(bug_report_file_path)
if bug_report_id in bug_reports:
current_bug_report_summary = bug_reports[bug_report_id]['bug_report']['summary']
else:
current_bug_report_summary = retrieve_summary(bug_reports, bug_report_full_sha)['bug_report']['summary']
feature_3_data = sparse.load_npz(data_prefix + '_' + bug_report_id + '_feature_3_data.npz')
with open(data_prefix + '_' + bug_report_id + '_feature_3_index_lookup', 'r') as feature_3_file:
feature_3_file_lookup = json.load(feature_3_file)
graph_data = sparse.load_npz(data_prefix + '_' + bug_report_id + '_graph_features_data.npz').tocsr()
with open(data_prefix + '_' + bug_report_id + '_graph_features_index_lookup', 'r') as graph_lookup_file:
graph_lookup = json.load(graph_lookup_file)
features_5_6_data = sparse.load_npz(data_prefix + '_' + bug_report_id[0:7] + '_features_5_6_data.npz').tocsr()
with open(data_prefix + '_' + bug_report_id[0:7] + '_features_5_6_index_lookup', 'r') as feaures_5_6_lookup_file:
features_5_6_lookup = json.load(feaures_5_6_lookup_file)
if bug_report_id in bug_reports:
fixed_filenames = bug_reports[bug_report_id[0:7]]['commit']['diff'].keys()
else:
fixed_filenames = retrieve_summary(bug_reports, bug_report_full_sha)['commit']['diff'].keys()
features = []
features_files = []
for file_index, current_file_sha in enumerate(shas):
current_lookup = lookups[current_file_sha]
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[current_file_sha]
current_features_5_6 = features_5_6_lookup[current_file_sha]
current_file_name = sha_to_file_name[current_file_sha]
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_indexes, current_file_sha)
f3 = feature_3(feature_3_data, feature_3_file_lookup, current_file_sha)
f4 = feature_4(current_bug_report_summary, ast_cache_collection, current_file_sha)
f5 = (features_5_6_data[current_features_5_6, 0])
f6 = (features_5_6_data[current_features_5_6, 1]) / max_frequency
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]
if current_file_name in fixed_filenames:
used_in_fix = 1.0
else:
used_in_fix = 0.0
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(current_file_sha)
ast_cache_collection.close()
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)
def load_enriched_api(data_prefix, commit):
bug_report_id = commit[0:7]
data = sparse.load_npz(data_prefix+'_'+bug_report_id+'_tfidf_enriched_api.npz')
with open(data_prefix+'_'+bug_report_id+'_partial_enriched_api_index_lookup', 'r') as lookup_file:
indexes = json.load(lookup_file)
return (data, indexes)
if __name__ == '__main__':
main()