arXiv:2607. 09224v1 Announce Type: cross Abstract: Version control systems are essential for collaborative software development, yet tools like git remain challenging for many practitioners.
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.
ComBeautify is a tool that automatically generates Conventional Commits Specification (CCS)-compliant commit messages by combining context-preserving summarization of Git diffs with large language models. It learns commit categories from historical data rather than relying on manually curated definitions, and also produces the CCS description component. Evaluation on 13 languages, 6,500 repositories, and over 20 million commits shows that project-specific training can outperform generic baselines by up to three times, with summarization accuracy reaching 95.25% on a manually curated dataset.
The study surveys popular, active GitHub repositories across thirteen languages, treating each repository’s AGENTS.md and CLAUDE.md files as instruction sets for coding agents. It finds that most repositories include such instructions, with CLAUDE.md often referencing AGENTS.md, and that these files contain a mix of project knowledge and workflow rules, including varying stances on AI attribution. The analysis reveals that more popular projects tend to have stricter pull‑request rules and AI contribution guidelines, and that LLM and agent projects are over‑represented among adopters.
arXiv:2609.07586v1 Announce Type: new Abstract: Software repositories contain vast amounts of data on code contributions, bug reports, and project activities, yet this information remains challenging...
E2E-SWE is a benchmark that tests large language models’ ability to create complete, functional software repositories from scratch. It includes 186 tasks across 11 programming languages, each requiring an agent to build an installable project based solely on a natural‑language specification and an empty workspace, while passing a hidden test suite. The benchmark was crafted by software engineers and LLMs, then refined through iterative verification by autonomous agents to ensure clarity and solvability.
arXiv:2608. 09072v1 Announce Type: cross Abstract: Large language model-powered coding agents are increasingly used to modify existing code repositories, for example, by adding features or fixing bugs.
arXiv:2608. 04783v1 Announce Type: cross Abstract: The integration of Large Language Models (LLMs) into software engineering has shifted the focus from function-level generation to repository-scale assistance.