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RELAY: Scaling Historical Build Restoration through Rule Reuse and Agentic Repair — arXiv:2610.27784

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.27784
License (as archived): CC0-1.0
Archived: 2026-10-21T07:08:04.401Z
SHA-256 of the archived text: f52cedadd6760230279bda1bf6bece13b74ecec8585e79c56bbadd73ba7437e1
Stable URL of this page: https://metraipolis.com/documento/dc_ec8afaaa6178

How to cite

RELAY: Scaling Historical Build Restoration through Rule Reuse and Agentic Repair — arXiv:2610.27784 — arXiv (en), https://arxiv.org/abs/2610.27784 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ec8afaaa6178 (Metraipolis archive dc_ec8afaaa6178, sha256 f52cedadd6760230279bda1bf6bece13b74ecec8585e79c56bbadd73ba7437e1)

BibTeX

@misc{dcec8afaaa6178,
  title = {RELAY: Scaling Historical Build Restoration through Rule Reuse and Agentic Repair — arXiv:2610.27784},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.27784},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ec8afaaa6178; sha256 f52cedadd6760230279bda1bf6bece13b74ecec8585e79c56bbadd73ba7437e1},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.27784. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Vulnerability benchmarks and software engineering research depend on rebuilding historical software. Yet builds rely on external resources, such as base images and packages, that change independently of the source code. In this study, we examine this environment decay on 14,159 OSS-Fuzz cases. We find that 69.7% no longer complete reconstruction and verification, and that most failures share a small number of recurring failure symptoms. A recurring failure can therefore be diagnosed once and its repair reused. We present Relay, a hybrid framework that uses an LLM to derive and validate deterministic repair rules from representative failures, reuses them without model calls, and escalates cases unresolved by rules to a per-case agent. We evaluate Relay in human-guided and fully automated rule-derivation campaigns and compare it with a per-case agent approach. We find that rule derivation can be fully automated, and that reusable rules restore as many builds as per-case agents with fewer build executions. However, agents restore cases that the rules cannot. Our results show that a hybrid approach that applies rules first and escalates the remaining cases to an agent restores more builds than either approach alone and at a lower cost than using agents alone.

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/2610.27784 — 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 2026-10-21T07:05:43.599Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).