mirror of
https://github.com/google-gemini/gemini-cli.git
synced 2026-06-11 11:57:03 -07:00
Coco Sheng
96 lines
3.8 KiB
Markdown
96 lines
3.8 KiB
Markdown
# Backlog Analysis Toolkit
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This directory contains a suite of AI-powered tools for analyzing GitHub issues
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and determining implementation effort levels for the Gemini CLI project.
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## 📁 Directory Structure
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- `data/`: Contains the issue data in JSON and CSV formats.
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- `bugs.json`: The primary source of truth for bug analysis.
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- `utils/`: Auxiliary scripts for manual overrides, debugging, and post-analysis
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validation (e.g., `inject_manual_fixes.py`).
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- `analyze_pipeline.py`: A unified Python script that orchestrates the entire
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effort analysis pipeline end-to-end, combining agentic analysis, single-turn
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fallbacks, heuristic validation, and CSV export.
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- `generic_processor.py`: A highly configurable agent for custom backlog tasks.
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## 🚀 The Ideal Workflow
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### Step 1: Categorize via GitHub CLI & Export to JSON
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If you have a raw list of uncategorized issues, the first step is to apply the
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correct types (`bug` or `feature`) directly on GitHub, and then fetch the data
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into a local JSON file for analysis.
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**A) Auto-Categorize on GitHub:** We provide a dedicated Python script that will
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automatically fetch uncategorized issues matching your search URL, classify them
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using the Gemini API, and apply the correct labels and title prefixes (`[Bug]`
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or `[Feature]`) directly on GitHub.
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```bash
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python3 categorize_issues.py "https://github.com/google-gemini/gemini-cli/issues/?q=-label:type/bug+-label:type/feature+is:open" --api-key "YOUR_KEY" --limit 50
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```
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**B) Export to JSON:** Once the issues are correctly labeled on GitHub, fetch
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them into a local JSON file. You can simply copy a GitHub search URL from your
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browser:
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```bash
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# Fetch bugs
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python3 fetch_from_url.py "https://github.com/google-gemini/gemini-cli/issues/?q=type%3ABug+is%3Aopen" --output data/bugs.json
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# Fetch features
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python3 fetch_from_url.py "https://github.com/google-gemini/gemini-cli/issues/?q=type%3AFeature+is%3Aopen" --output data/issues.json
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```
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### Step 2: Analyze Effort Level
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Run the unified effort analysis pipeline. This single Python script efficiently
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runs a fast, context-aware single-turn analysis for each issue (pre-fetching
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codebase context via grep), dynamically validates the effort level against
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architectural rules using an AI reviewer persona, and immediately exports the
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results to a CSV.
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```bash
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python3 analyze_pipeline.py --api-key "YOUR_KEY" --input data/bugs.json --project ../../packages
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```
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### Step 3: Review and Update JSON
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The pipeline automatically updates your JSON file in place with the technical
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`analysis`, `effort_level`, and `reasoning`, and exports a `.csv` file.
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If you need to perform additional bulk updates or custom processing on the
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resulting JSON (like grouping by package or identifying related PRs), use the
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Generic Processor:
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```bash
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python3 generic_processor.py \
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--api-key "YOUR_KEY" \
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--input data/bugs.json \
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--output data/bugs_updated.json \
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--project ../../packages \
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--prompt "Analyze these issues and add a 'target_package' field to each JSON object based on the codebase analysis."
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```
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## 🧠 Effort Level Criteria
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Ratings are based on technical complexity and reproduction difficulty:
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- **Small (1 day):** Trivial logic changes, localized fixes (1-2 files), easy to
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reproduce.
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- **Medium (2-3 days):** Requires tracing across multiple components, UI state
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management (React/Ink), or harder reproduction.
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- **Large (3+ days):** Architectural issues, platform-specific (Windows, PTY,
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Signals), performance bottlenecks, or core protocol changes.
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_Note: Any bug that is difficult to reproduce or platform-specific must not be
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rated as Small._
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## 🛠 Usage Notes
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- **API Key:** Ensure you have a valid Gemini API key set in the scripts.
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- **Paths:** Scripts are configured to look for data in the `data/` subdirectory
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and the codebase in `../../packages`.
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- **Requirements:** Requires Python 3 and `jq` (for the shell script).
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