Backlog Analysis Toolkit

This directory contains a suite of AI-powered tools for analyzing GitHub issues and determining implementation effort levels for the Gemini CLI project.

📁 Directory Structure

data/: Contains the issue data in JSON and CSV formats.
- bugs.json: The primary source of truth for bug analysis.
utils/: Auxiliary scripts for manual overrides, debugging, and post-analysis validation (e.g., inject_manual_fixes.py).
analyze_pipeline.py: A unified Python script that orchestrates the entire effort analysis pipeline end-to-end, combining agentic analysis, single-turn fallbacks, heuristic validation, and CSV export.
generic_processor.py: A highly configurable agent for custom backlog tasks.

🚀 The Ideal Workflow

Step 1: Categorize via GitHub CLI & Export to JSON

If you have a raw list of uncategorized issues, the first step is to apply the correct types (bug or feature) directly on GitHub, and then fetch the data into a local JSON file for analysis.

A) Auto-Categorize on GitHub: We provide a dedicated Python script that will automatically fetch uncategorized issues matching your search URL, classify them using the Gemini API, and apply the correct labels and title prefixes ([Bug] or [Feature]) directly on GitHub.

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

B) Export to JSON: Once the issues are correctly labeled on GitHub, fetch them into a local JSON file. You can simply copy a GitHub search URL from your browser:

# Fetch bugs
python3 fetch_from_url.py "https://github.com/google-gemini/gemini-cli/issues/?q=type%3ABug+is%3Aopen" --output data/bugs.json

# Fetch features
python3 fetch_from_url.py "https://github.com/google-gemini/gemini-cli/issues/?q=type%3AFeature+is%3Aopen" --output data/issues.json

Step 2: Analyze Effort Level

Run the unified effort analysis pipeline. This single Python script efficiently runs a fast, context-aware single-turn analysis for each issue (pre-fetching codebase context via grep), dynamically validates the effort level against architectural rules using an AI reviewer persona, and immediately exports the results to a CSV.

python3 analyze_pipeline.py --api-key "YOUR_KEY" --input data/bugs.json --project ../../packages

Step 3: Review and Update JSON

The pipeline automatically updates your JSON file in place with the technical analysis, effort_level, and reasoning, and exports a .csv file.

If you need to perform additional bulk updates or custom processing on the resulting JSON (like grouping by package or identifying related PRs), use the Generic Processor:

python3 generic_processor.py \
  --api-key "YOUR_KEY" \
  --input data/bugs.json \
  --output data/bugs_updated.json \
  --project ../../packages \
  --prompt "Analyze these issues and add a 'target_package' field to each JSON object based on the codebase analysis."

🧠 Effort Level Criteria

Ratings are based on technical complexity and reproduction difficulty:

Small (1 day): Trivial logic changes, localized fixes (1-2 files), easy to reproduce.
Medium (2-3 days): Requires tracing across multiple components, UI state management (React/Ink), or harder reproduction.
Large (3+ days): Architectural issues, platform-specific (Windows, PTY, Signals), performance bottlenecks, or core protocol changes.

Note: Any bug that is difficult to reproduce or platform-specific must not be rated as Small.

🛠 Usage Notes

API Key: Ensure you have a valid Gemini API key set in the scripts.
Paths: Scripts are configured to look for data in the data/ subdirectory and the codebase in ../../packages.
Requirements: Requires Python 3 and jq (for the shell script).

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