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IssueLighter: An Issue Type-Aware LLM Agent for Issue Report Validity Assessment — arXiv:2611.02721

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.02721
License (as archived): CC0-1.0
Archived: 2026-11-03T10:37:52.573Z
SHA-256 of the archived text: b90eae2e9f9589a7fce78a2854f32742f6fb7d68ddfebbe3d94c4d29af0a87a2
Stable URL of this page: https://metraipolis.com/documento/dc_751bf41f0281

How to cite

IssueLighter: An Issue Type-Aware LLM Agent for Issue Report Validity Assessment — arXiv:2611.02721 — arXiv (en), https://arxiv.org/abs/2611.02721 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_751bf41f0281 (Metraipolis archive dc_751bf41f0281, sha256 b90eae2e9f9589a7fce78a2854f32742f6fb7d68ddfebbe3d94c4d29af0a87a2)

BibTeX

@misc{dc751bf41f0281,
  title = {IssueLighter: An Issue Type-Aware LLM Agent for Issue Report Validity Assessment — arXiv:2611.02721},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.02721},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_751bf41f0281; sha256 b90eae2e9f9589a7fce78a2854f32742f6fb7d68ddfebbe3d94c4d29af0a87a2},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.02721. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Invalid issue reports are prevalent in open source software (OSS) projects, imposing substantial burdens on maintainers who already face a high risk of burnout. Prior research has primarily focused on identifying invalid reports for specific issue categories (e.g., bug and wontfix), leaving the broader landscape of OSS issue types largely unexplored and limiting the generalizability of existing approaches. Moreover, existing methods provide little support for explaining or validating their predictions, reducing their practical usefulness for maintainers. With the rise of LLM-based agents, it remains unclear whether a comprehensive understanding of the causes of invalidity across all issue types can improve validity assessment performance while producing well-grounded rationales. To this end, we first manually annotated the validity status of 520 issue reports from three OSS projects, i.e., pandas, scikit-learn, and mypy, resulting in 257 invalid reports and 263 valid ones. Then, we conducted a thematic analysis on these invalid reports and identified 13 causes of invalidity that are closely specific to issue types. Based on these insights, we propose IssueLighter, an agent-based framework with a type-aware validity assessment strategy that dynamically executes commands. Experimental results show that IssueLighter consistently and substantially outperforms all baseline methods, with an average F1 improvement of over 20 percentage points. Ablation studies confirm that both the type-aware strategy and the taxonomy of causes of invalidity contribute to the performance gains. Qualitative analysis further demonstrates that IssueLighter generates rationales grounded in accurately identified causes of invalidity and supported by evidence. Moreover, leveraging these rationales, we challenged maintainers' original decisions in four disputed cases, and all received approval.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Software Engineering (cs.SE) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.02721 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T10:29:48.891Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).