mirror of
https://github.com/google-gemini/gemini-cli.git
synced 2026-05-15 06:12:50 -07:00
Coco Sheng
99 lines
3.8 KiB
Markdown
99 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., `validate_effort.py`, `inject_manual_fixes.py`).
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- `*.py`: Core analysis and export scripts (e.g., `bug_analyzer_final.py`,
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`generate_bugs_csv.py`).
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- `run_pipeline.sh`: A shell script that orchestrates the entire effort analysis
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pipeline end-to-end.
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## 📥 Prerequisites: Data Generation
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Before running the analyzers, you must fetch the issue data from GitHub. The
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scripts expect the data in JSON format.
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The easiest way to generate this is to simply copy the URL from your browser
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when looking at a filtered list of issues on GitHub, and pass it to our fetcher
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script.
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_(Note: You must have the [GitHub CLI (`gh`)](https://cli.github.com/) installed
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and authenticated)._
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```bash
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# Fetch any filtered list of issues directly from a GitHub URL
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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 to a different file
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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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## 🚀 Workflows
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### 1. Auto-Categorizing Issues with Gemini CLI
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If you have a list of uncategorized issues fetched from GitHub, your first step
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should be to classify them. You can use the Gemini CLI directly in your terminal
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to label them on GitHub.
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**Example command:**
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```bash
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gemini "Read data/uncategorized.json. For each issue, determine if it is a bug or a feature request. Then, use the gh CLI tool to add either the 'type/bug' or 'type/feature' label directly to the issue on GitHub."
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```
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_Note: Make sure your `gemini-cli` has permission to execute shell commands if
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you want it to apply the labels automatically via `gh`._
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### 2. Full Effort Analysis Pipeline
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Instead of running individual steps manually, you can run the entire analysis
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pipeline (Initial Triage -> Deep Agentic Analysis -> Iterative Recovery ->
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Validation -> CSV Export) with a single command.
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```bash
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GEMINI_API_KEY="YOUR_KEY" ./run_pipeline.sh data/bugs.json ../../packages
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```
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### 3. Generic Issue Processing
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For any other backlog task (e.g., categorizing features, updating labels, or
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custom analysis), use the `generic_processor.py`. This script allows you to
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provide a custom system prompt and a project root for codebase context.
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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/features.json \
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--output data/features_analyzed.json \
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--project ../../packages \
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--prompt "Analyze these features and suggest which package they belong in. Output JSON: {\"package\": \"name\"}"
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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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