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Active-SWE: Benchmarking Coding Agents for Proactive Bug Fixing without Issue Reports — arXiv:2608.04682

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.04682
License (as archived): CC0-1.0
Archived: 2026-10-31T11:14:32.099Z
SHA-256 of the archived text: c371dbb03eeccca40570a7157af8731012026f7c126341796c25b4a99fe52487
Stable URL of this page: https://metraipolis.com/documento/dc_98cf748422ea

How to cite

Active-SWE: Benchmarking Coding Agents for Proactive Bug Fixing without Issue Reports — arXiv:2608.04682 — arXiv (en), https://arxiv.org/abs/2608.04682 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_98cf748422ea (Metraipolis archive dc_98cf748422ea, sha256 c371dbb03eeccca40570a7157af8731012026f7c126341796c25b4a99fe52487)

BibTeX

@misc{dc98cf748422ea,
  title = {Active-SWE: Benchmarking Coding Agents for Proactive Bug Fixing without Issue Reports — arXiv:2608.04682},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.04682},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_98cf748422ea; sha256 c371dbb03eeccca40570a7157af8731012026f7c126341796c25b4a99fe52487},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.04682. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Coding agents powered by large language models (LLMs) are increasingly adopted in software engineering (SWE) scenarios, capable of fixing a specific bug in large-scale codebase. However, existing SWE benchmarks typically assume that high-quality issue reports with detailed information are always available, which is easily violated in practice due to the complexity of report acquisition and curation. To address this, we introduce Active-SWE, a benchmark for evaluating coding agents on proactively discovering and fixing multiple bugs without report guidance, covering 1,663 tasks across six bug categories and eight languages. Beyond shifting the focus from existing reactive bug fixing to proactive bug fixing, Active-SWE enables a more in-depth evaluation by expanding the scope from fixing a specific recorded bug to multiple-bug fixing and potential bug discovery scenarios. To construct Active-SWE, we propose a novel difficulty-aware task formulation pipeline with a dual-track evaluation framework, facilitating comprehensive evaluation of proactive bug-fixing capability. Extensive experiments reveal that most state-of-the-art coding agents struggle with proactive bug-fixing tasks, demonstrating limited performance in locating and resolving recorded bugs, handling multiple bug fixing scenarios, and discovering valid potential bugs.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-06 in cs.AI).
Categorie arXiv: Software Engineering (cs.SE) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2608.04682 — 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 2026-10-31T11:02:13.538Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-08-06?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).