⚡ Bolt: optimize keyword extraction in TrendAnalyzer#832
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Optimized the keyword extraction logic in `TrendAnalyzer` to improve bulk analysis performance. - Batched string operations by joining descriptions before calling `.lower()` once. - Pre-compiled the word extraction regex at the class level. - Simplified regex pattern from `\b\w+\b` to `\w+` for faster execution. Performance impact: ~21% improvement in extraction speed. Verified with benchmarks and full test suite.
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Pull request overview
This PR optimizes TrendAnalyzer keyword extraction for bulk issue analysis by reducing repeated string operations and avoiding repeated regex compilation, improving runtime for large issue sets.
Changes:
- Batch description normalization by joining first and calling
.lower()once. - Use a precompiled word-extraction regex for tokenization.
- Document the optimization learning in
.jules/bolt.md.
Reviewed changes
Copilot reviewed 2 out of 2 changed files in this pull request and generated 2 comments.
| File | Description |
|---|---|
| backend/trend_analyzer.py | Optimizes keyword extraction by batching .lower() and reusing a compiled word regex. |
| .jules/bolt.md | Adds a Bolt note capturing the keyword extraction optimization guidance. |
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| words = re.findall(r'\b\w+\b', text) | ||
| # Optimization: Join first, then lower once to reduce string method overhead. | ||
| # This is significantly faster for large lists of issues. | ||
| raw_text = " ".join([issue.description for issue in issues if issue.description]) |
| # Pre-compile regex for word extraction to improve performance in bulk analysis | ||
| self._word_re = re.compile(r'\w+') |
![cubic-dev-ai[bot]](https://avatars.githubusercontent.com/in/1082092?s=60&v=4){kind=small}
💡 What: Optimized the keyword extraction logic in the
TrendAnalyzerclass.🎯 Why: The previous implementation called
.lower()on every individual issue description within a loop and usedre.findallwith a non-precompiled pattern containing word boundary anchors (\b). For bulk analysis of thousands of issues, these repeated operations created significant overhead.📊 Impact: Reduces execution time for keyword extraction by approximately 21.7%.
🔬 Measurement: Verified using a temporary benchmark script that executed the extraction logic 100 times over 2000 mock issues.
Correctness was verified by running the full backend test suite (
pytest), as well as root and frontend tests to ensure no regressions.PR created automatically by Jules for task 10565139700722544707 started by @RohanExploit
Summary by cubic
Optimized keyword extraction in
TrendAnalyzerfor bulk analysis by batching string normalization and using a precompiled word regex, cutting extraction time ~22% on large issue sets..lower()once instead of per item.\w+and use it for tokenization (replacing\b\w+\b).Written for commit a3d1ba7. Summary will update on new commits.