feat(jira): implement quality metrics for Jira issues (Feature 003) #4
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Add comprehensive quality metrics calculation and export for Jira issues: Issue-level metrics (10 new columns in jira_issues_export.csv): - cycle_time_days: Days from creation to resolution - aging_days: Days since creation for open issues - comments_count: Total number of comments - description_quality_score: Quality score 0-100 based on length, AC, formatting - acceptance_criteria_present: Detection of Given/When/Then, AC:, checkboxes - comment_velocity_hours: Time to first comment - silent_issue: Issues with no comments - same_day_resolution: Resolved on creation day - cross_team_score: Collaboration score based on distinct commenters - reopen_count: Status transitions from Done to non-Done New aggregated CSV exports: - jira_project_metrics.csv: Per-project aggregations (14 columns) - jira_person_metrics.csv: Per-assignee metrics (6 columns) - jira_type_metrics.csv: Per-issue-type metrics (5 columns) Implementation details: - MetricsCalculator class with TDD approach (665 tests, 92% coverage) - JiraMetricsExporter for aggregated CSV exports - get_issue_changelog() with graceful 403/404 degradation - CLI integration: metrics auto-calculated with --sources jira
Summary of ChangesHello @amargiovanni, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request significantly enhances the Jira integration by introducing a robust system for calculating and exporting various quality metrics. It provides granular insights into individual issue quality, team performance, and project health through new and extended CSV reports. The changes aim to empower users with deeper analytical capabilities for their Jira data, covering aspects from issue lifecycle to collaboration patterns. Highlights
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Code Review
This is an excellent and comprehensive pull request that adds significant value with the new Jira quality metrics. The implementation is well-structured, following a clear plan laid out in the specification documents. The code is robust, with good error handling (especially the graceful degradation for the changelog API) and thorough test coverage, including unit, integration, and fixture updates. I've included a few minor suggestions to improve maintainability by enhancing type specificity and reducing code duplication, but overall, this is a very high-quality contribution.
src/github_analyzer/cli/main.py
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| output.log("Fetching comments...", "info") | ||
| all_comments = [] | ||
| issue_comments_map: dict[str, list] = {} # Map issue key to comments |
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The type hint for issue_comments_map is dict[str, list], which is a bit vague. For better type safety and readability, you could use a more specific type like dict[str, list[JiraComment]]. A similar improvement can be made for issues_by_project on line 744 to dict[str, list[IssueMetrics]].
| issue_comments_map: dict[str, list] = {} # Map issue key to comments | |
| issue_comments_map: dict[str, list[JiraComment]] = {} # Map issue key to comments |
| @staticmethod | ||
| def _format_float(value: float | None) -> str: | ||
| """Format float with 2 decimal places, or empty string if None.""" | ||
| if value is None: | ||
| return "" | ||
| return f"{value:.2f}" |
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This _format_float static method is a duplicate of the one in jira_exporter.py. To avoid code duplication and ensure consistent formatting logic, consider moving this helper to a shared utility module (e.g., src/github_analyzer/exporters/utils.py) and importing it in both exporter files.
Add type annotation for changelog values return to satisfy mypy's no-any-return check.
Codecov Report❌ Patch coverage is
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Improve type safety by replacing generic `dict[str, list]` with specific types `dict[str, list[JiraComment]]` and `dict[str, list[IssueMetrics]]`.
Summary
New CSV Exports
Issue-Level Metrics
Test Plan