data_extraction_transformation/scripts/extract-bugs-bugbug.py

from bugbug import bugzilla, db, bug_features
import pandas as pd
from helper import append_strings, get_json, txt_to_list
import os
import pandas as pd
import shutil
import argparse

def parse_args():
    parser = argparse.ArgumentParser(description="Script to cross-checkif the signatures are corrct or not")
    parser.add_argument('-a', '--alert-file', required=True, help="Path to the alerts file")
    parser.add_argument('-o', '--output-location', required=True, help="Path the location of the bugs CSV")
    return parser.parse_args()

'''
This function is dedicated for extracting JSON attrbutes that do not exist always with exception handling
'''
def process_element(the_json, the_attribute):
    try:
        return the_json[the_attribute]
    except:
        return ""

'''
Thsi function extracts the attributes of a given bug from the provided JSON and labels it as a performance-related bug or not. It reutrns a dictionary to be appended into the bug dataframe to be converted into a CSV
'''
def extract_row(json_bug, isperfbug):
    bug_row = {'bug_id': process_element(json_bug, 'id'),
    'bug_resolution': process_element(json_bug, 'resolution'),
    'bug_type': process_element(json_bug, 'type'),
    'bug_component': process_element(json_bug, 'component'),
    'bug_summary': process_element(json_bug, 'summary'),
    'bug_classification': process_element(json_bug, 'classification'),
    'bug_status': process_element(json_bug, 'status'),
    'bug_creation_time': process_element(json_bug, 'creation_time'),
    'bug_url': process_element(json_bug, 'url'),
    'bug_last_change_time': process_element(json_bug, 'last_change_time'),
    'bug_severity': process_element(json_bug, 'severity'),
    'bug_priority': process_element(json_bug, 'priority'),
    'bug_product': process_element(json_bug, 'product'),
    'bug_is_confirmed': process_element(json_bug, 'is_confirmed'),
    'bug_votes': process_element(json_bug, 'votes'),
    'bug_is_open': process_element(json_bug, 'is_open'),
    'bug_assigned_to': process_element(json_bug, 'assigned_to'),
    'bug_cf_last_resolved': process_element(json_bug, 'cf_last_resolved'),
    'bug_cf_performance_impact': process_element(json_bug, 'cf_performance_impact'),
    'bug_version': process_element(json_bug, 'version'),
    'bug_whiteboard': process_element(json_bug, 'whiteboard'),
    'bug_platform': process_element(json_bug, 'platform'),
    'bug_keywords': append_strings(json_bug['keywords']),
    'IsPerformanceBug': isperfbug
    }
    return bug_row



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)



if __name__ == "__main__":
    main()