def main()

in data_extraction_transformation/scripts/extract-bugs-bugbug.py [0:0]

• 51 lines of code
• 4 McCabe index (conditional complexity)

def main():
    args = parse_args()
    alert_file = args.alert_file
    output_location = args.output_location

    '''
    The following list contains the columns names of the CSV to be generated through this script
    '''
    columns = [
        'bug_id',
        'bug_resolution',
        'bug_type',
        'bug_component',
        'bug_summary',
        'bug_classification',
        'bug_status',
        'bug_creation_time',
        'bug_url',
        'bug_last_change_time',
        'bug_severity',
        'bug_priority',
        'bug_product',
        'bug_is_confirmed',
        'bug_votes',
        'bug_is_open',
        'bug_assigned_to',
        'bug_cf_last_resolved',
        'bug_cf_performance_impact',
        'bug_version',
        'bug_whiteboard',
        'bug_platform',
        'bug_keywords',
        'IsPerformanceBug'
    ]


    '''
    Downlanding the latest version of bugs data using bugbug
    '''
    db.download(bugzilla.BUGS_DB)
    df = pd.DataFrame(columns=columns)
    alerts_df = pd.read_csv(alerts_file)
    '''
    Only bugs that are asosciated with alerts extracted through extract-alerts.py will be kept. note that bugs.txt contains the IDs of bugs associated with alerts obtained from the same Python script previously mentioned
    '''
    bugs_ids = alerts_df['alert_bug_number'].unique().flatten()
    for bug in bugzilla.get_bugs():
        if str(bug["id"]) in bugs_ids:
            isperfbug = False
            if(bug_features.IsPerformanceBug().__call__(bug)):
                isperfbug = True
            new_row = extract_row(bug, isperfbug)
            df = pd.concat([df, pd.DataFrame([new_row])], ignore_index=True)
    df.to_csv(output_location + '/bugs_data.csv', index=False)