Evaluate pure lookup generator
Nobody has claimed this yet.
Assessment
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Newbie friendliness
- 48/100
- Issue type
- Feature
- Clarity
- Mostly clear
- Activity status
- Stale
- Tech stack
- python
- Domain
- machine-learning, testing
Research direction
Start by locating the pure n-gram lookup generator and the test-set entry point in the repository. Add the evaluation script under scripts/, record results in experiments/lookup-baseline/, and produce the requested analysis notebook or markdown report. Done means the listed accuracy, BLEU-4, coverage, error-analysis, and latency metrics are reported.
Written by the indexing model from the issue text.
Description
Summary
Evaluate the pure n-gram lookup generator on the test set to establish a baseline.
Success Criteria
- Exact match accuracy measured
- Type/scope/subject accuracy measured separately
- Coverage: % of test cases where lookup has high confidence
- Error analysis: categorize failure modes
- Latency benchmarked (p50, p95, p99)
Metrics
| Metric | Description |
|---|---|
| Exact Match | Full commit message matches |
| Type Accuracy | Correct commit type |
| Scope Accuracy | Correct scope (when applicable) |
| BLEU-4 | N-gram overlap score |
| Coverage | % of cases with confidence > threshold |
Deliverables
- Evaluation script in scripts/
- Results saved to experiments/lookup-baseline/
- Analysis notebook or markdown report
This is a good first experiment!
Clear scope, measurable outcomes, introduces experiment infrastructure.
- Dominant language
- Python
- Stars
- 0
- Forks
- 0
- PR merge metrics
- No merged PRs in 30d
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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