arXiv:2607. 13111v1 Announce Type: cross Abstract: Distinguishing semantic-preserving commits from changing ones remains an open challenge in software repository mining.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2508. 12232v5 Announce Type: replace-cross Abstract: Issue-to-commit link recovery in software repositories is fundamental to software traceability and project management, yet it remains a challenging task.
arXiv:2607. 02469v1 Announce Type: cross Abstract: Software tests and code evolve together: a code change should be followed by new or updated tests that record the new software behavior.
The paper investigates how machine‑learning models can determine whether a claim (a test assertion) remains valid after a code change. It compares two questioning strategies: asking whether a diff preserves behavior versus asking whether a specific claim still holds. The authors find that the latter approach yields far higher precision (up to 0.974) across models of varying cost, while the former performs poorly (precision 0.291–0.329). They also benchmark against a regression‑test selector, showing that even near‑complete knowledge of a change’s reach does not reliably identify falsified claims. The study is grounded in 10,369 mined claims with 184 execution‑verified flips from 23 Python libraries.
arXiv:2609. 31176v1 Announce Type: new Abstract: Repository-level issue localization aims to identify and rank the files and functions relevant to resolving a reported issue.
arXiv:2507. 16395v3 Announce Type: replace Abstract: Atomic commits, which address a single development concern, are a best practice in software development.
arXiv:2609.13728v1 Announce Type: cross Abstract: Historical Linux kernel patches capture defect knowledge that applies beyond their original repair sites. Recent work has shown that large language m...