gistfile1.txt

import csv
from jira import JIRA

# Configuration for both instances
DC_SERVER = 'https://your-datacenter-jira-instance.com'
DC_API_TOKEN = 'your_dc_api_token'
DC_EMAIL = 'your_dc_email@example.com'

CLOUD_SERVER = 'https://your-cloud-jira-instance.atlassian.net'
CLOUD_API_TOKEN = 'your_cloud_api_token'
CLOUD_EMAIL = 'your_cloud_email@example.com'

# Connect to Jira instances
jira_dc = JIRA(server=DC_SERVER, token_auth=DC_API_TOKEN)
jira_cloud = JIRA(server=CLOUD_SERVER, basic_auth=(CLOUD_EMAIL, CLOUD_API_TOKEN))

# Fetch recent issues with large footprints
def fetch_issues(jira_instance, jql, max_issues=50):
    return jira_instance.search_issues(jql, maxResults=max_issues, fields="summary,description,attachment,issuelinks,comment,subtasks")

# Extract issue details

def extract_issue_data(issue):
    return {
        'key': issue.key,
        'summary': issue.fields.summary,
        'description_length': len(issue.fields.description or ''),
        'attachment_count': len(getattr(issue.fields, 'attachment', [])),
        'link_count': len(getattr(issue.fields, 'issuelinks', [])),
        'comment_count': issue.fields.comment.total if hasattr(issue.fields.comment, 'total') else 0,
        'subtask_count': len(issue.fields.subtasks or [])
    }

# Compare two lists of issues
def compare_issues(dc_issues, cloud_issues):
    matches = 0
    comparisons = []
    for dc_issue in dc_issues:
        dc_data = extract_issue_data(dc_issue)
        try:
            cloud_issue = next(i for i in cloud_issues if i.key == dc_issue.key)
            cloud_data = extract_issue_data(cloud_issue)
            similarity = all(dc_data[k] == cloud_data[k] for k in dc_data if k != 'key')
            matches += similarity
            comparisons.append({**dc_data, **{f'cloud_{k}': cloud_data[k] for k in cloud_data if k != 'key'}, 'match': similarity})
        except StopIteration:
            comparisons.append({**dc_data, 'cloud_missing': True, 'match': False})
    return comparisons, matches / len(dc_issues) * 100 if dc_issues else 0

# Write results to CSV
def write_comparison_csv(data, filename):
    if not data:
        return
    headers = sorted(set(k for row in data for k in row))
    with open(filename, mode='w', newline='', encoding='utf-8') as f:
        writer = csv.DictWriter(f, fieldnames=headers)
        writer.writeheader()
        for row in data:
            writer.writerow(row)

# Catalog active sprints per project
def catalog_active_sprints(jira_instance, suffix):
    boards = jira_instance.boards()
    sprint_data = []
    for board in boards:
        sprints = jira_instance.sprints(board.id, state='active')
        for sprint in sprints:
            sprint_data.append({'project': board.name, 'sprint_id': sprint.id, 'sprint_name': sprint.name})
    write_comparison_csv(sprint_data, f'active_sprints_{suffix}.csv')

# Main assessment logic
dc_issues = fetch_issues(jira_dc, 'ORDER BY updated DESC', 50)
cloud_issues = fetch_issues(jira_cloud, 'ORDER BY updated DESC', 100)
comparison, similarity_score = compare_issues(dc_issues, cloud_issues)

write_comparison_csv(comparison, 'issue_comparison.csv')
catalog_active_sprints(jira_dc, 'dc')
catalog_active_sprints(jira_cloud, 'cloud')

print(f"Cloud is {similarity_score:.2f}% like DC based on recent issues.")