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70 lines
1.9 KiB
Markdown
70 lines
1.9 KiB
Markdown
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# 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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- `issues.json`: General issue backlog.
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- `*.py`: Analysis and utility scripts.
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- `loop_analyzer.sh`: A shell script for running iterative analysis until all
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issues are processed.
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## 🚀 Workflows
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### 1. Initial Triage (Static)
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Use this for a quick, first-pass estimation.
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```bash
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python3 analyze_bugs.py
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```
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### 2. Deep Agentic Analysis
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Uses Gemini as an agent with access to the codebase.
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```bash
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python3 bug_analyzer_final.py
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```
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### 3. Iterative Analysis
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Runs the single-turn analyzer in a loop until all issues have a valid analysis.
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```bash
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./loop_analyzer.sh
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```
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### 4. Validation & Export
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Run these after analysis to ensure consistency and generate a readable report.
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```bash
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python3 validate_effort.py
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python3 generate_bugs_csv.py
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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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