IssueLighter: An Issue Type-Aware LLM Agent for Issue Report Validity Assessment
Story 19, from arXiv
IssueLighter is a new LLM-based tool that improves the accuracy of identifying invalid issue reports in open source projects by considering issue types and providing explainable rationales.
Key points
- IssueLighter addresses the problem of invalid issue reports in OSS by introducing a type-aware validity assessment strategy.
- The tool was tested on 520 issue reports from pandas, scikit-learn, and mypy, with 257 labeled as invalid.
- It identifies 13 specific causes of invalidity tied to issue types and uses them to improve performance.
- Experiments show an average F1 improvement of over 20 percentage points compared to baselines.
- Qualitative analysis confirms that IssueLighter generates well-supported rationales for its predictions.
- The tool was used to challenge maintainers' decisions in four cases, all of which were accepted.
Why it matters
Maintainers of open source projects face a high volume of invalid issue reports, which waste time and contribute to burnout. IssueLighter offers a more accurate and explainable way to assess report validity, helping maintainers focus on meaningful work.
Counterpoint
The tool's effectiveness depends on the quality and diversity of the annotated data used for training, which may limit its generalizability to other projects or issue types.
Sources
This summary was written by a language model and may contain errors. Read the original for the full story.