def main():
project_info = ProjectInfo('data/CS 169 F16 Projects - Sheet1.csv',
'proj-info')
analyzer = ProcessMiningAnalyzer(project_info)
# analyzer.ftype_count()
# analyzer.frequent_pattern()
analyzer.neighbor_selection()
def main():
project_info = ProjectInfo('data/CS 169 F16 Projects - Sheet1.csv', 'proj-info')
with open('conf/tokens.json', 'r') as f_in:
tokens = json.load(f_in)
for proj in project_info:
metric = MetricGithub(proj, tokens)
print(metric.metrics())
def main():
project_info = ProjectInfo('data/CS 169 F16 Projects - Sheet1.csv',
'proj-info')
with open('conf/tokens.json', 'r') as f_in:
tokens = json.load(f_in)
analyzer = IntegrationAnalyzer(tokens, project_info)
# print(len(analyzer.builds(project_info[0])))
data_cache, log_info = {}, {}
if 'trend_data.json' in os.listdir('cache'):
with open('cache/trend_data.json', 'r') as f_in:
data_cache = json.load(f_in)
if 'log_info.json' in os.listdir('cache'):
with open('cache/log_info.json', 'r') as f_in:
log_info = json.load(f_in)
try:
for proj in project_info:
if proj['ID'] in data_cache and proj['ID'] in log_info:
print('Skip Project {}'.format(proj['ID']))
continue
print('Processing Project {}'.format(proj['ID']))
data = analyzer.trend(proj, reload=False)
data_cache[proj['ID']] = data
log_info[proj['ID']] = data['log_info']
finally:
with open('cache/trend_data.json', 'w') as f_out:
json.dump(data_cache, f_out)
with open('cache/log_info.json', 'w') as f_out:
json.dump(log_info, f_out)
def main():
project_info = ProjectInfo('data/CS 169 F16 Projects - Sheet1.csv',
'proj-info')
with open('conf/tokens.json', 'r') as f_in:
tokens = json.load(f_in)
for proj in tqdm(project_info):
metric = MetricTracker(proj, token=tokens)
metric.metrics()
def main():
project_info = ProjectInfo('data/CS 169 F16 Projects - Sheet1.csv', 'proj-info')
iteration_grading = IterationGrading('data/', 'detailed')
with open('conf/tokens.json', 'r') as f_in:
tokens = json.load(f_in)
analyzer = MetricComparisonAnalyzer(tokens, project_info, iteration_grading)
# analyzer.comparison(project_info[0])
analyzer.correlation()
def main():
project_info = ProjectInfo('data/CS 169 F16 Projects - Sheet1.csv', 'proj-info')
with open('conf/tokens.json', 'r') as f_in:
tokens = json.load(f_in)
analyzer = TestAnalyzer(tokens, project_info)
for proj in tqdm(project_info):
# analyzer.cucumber_scenarios(proj)
analyzer.lifecycle(proj)
def main():
with open('conf/tokens.json', 'r') as f_in:
tokens = json.load(f_in)
project_info = ProjectInfo('data/CS 169 F16 Projects - Sheet1.csv',
'project-info')
analyzer = PivotalTrackerAnalyzer(project_info,
tokens['pivotal_tracker']['token'])
# analyzer.story_assign_plot()
analyzer.iteration_points()
def main():
with open('conf/tokens.json', 'r') as f_in:
token = json.load(f_in)
project_info = ProjectInfo('data/CS 169 F16 Projects - Sheet1.csv',
'proj-info')
analyzer = GithubAnalyzer(token['github']['token'], project_info)
# analyzer.commits_plot()
# analyzer.commmits_per_student_plot()
analyzer.iteration_commits()
def main():
pr_data = PeerReview('data/', 'peer_combined')
pr_analyzer = PeerReviewAnalyzer(pr_data)
with open('conf/tokens.json', 'r') as f_in:
tokens = json.load(f_in)
proj_info = ProjectInfo('data/CS 169 F16 Projects - Sheet1.csv', 'proj-info')
pt_analyzer = PivotalTrackerAnalyzer(proj_info, tokens['pivotal_tracker']['token'])
analyzer = PtPrComparisonAnalyzer(pr_analyzer, pt_analyzer)
# analyzer.generate_student_map()
analyzer.consistency_plot()
def main():
with open('conf/tokens.json', 'r') as f_in:
tokens = json.load(f_in)
project_info = ProjectInfo('data/CS 169 F16 Projects - Sheet1.csv',
'proj-info')
peer_review = PeerReview('data/', 'peer_combined')
gt_analyzer = GithubAnalyzer(tokens['github']['token'], project_info)
pt_analyzer = PivotalTrackerAnalyzer(tokens['pivotal_tracker']['token'],
project_info)
pr_analyzer = PeerReviewAnalyzer(peer_review)
analyzer = CombinedAnalyzer(gt_analyzer=gt_analyzer,
pt_analyzer=pt_analyzer,
pr_analyzer=pr_analyzer)
# analyzer.workload_correlation_plot(w_type='num_commits_normalized')
analyzer.workload_correlation_plot(w_type='file_edit_normalized')
# analyzer.workload_correlation_plot(w_type='line_edit')
analyzer.prediction('file_edit_normalized')
def main():
project_info = ProjectInfo('data/CS 169 F16 Projects - Sheet1.csv',
'proj-info')
analyzer = ProcessSegmentAnalyzer(project_info)
# analyzer.git_commit_overlaps()
analyzer.story_time_overlaps()