arXiv:2507. 16395v3 Announce Type: replace Abstract: Atomic commits, which address a single development concern, are a best practice in software development.
By Bo Hou, Xin Tan, Kai Zheng, Fang Liu, Yinghao Zhu, Li Zhang
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.
By Xin Zhou, Chun Yong Chong, Kisub Kim, Yun Peng, Rui Shu, Zihan Wu, Xu Han, Guowen Yuan, Zeyang Zhuang, Jounghoon Kim, Jeongjin Ju, Seongmin Ju, Taein Yoon, David Lo
arXiv:2606. 30573v1 Announce Type: new Abstract: We introduce SWE-Interact, a new testbed for evaluating coding agents on multi-turn, interactive, user-driven software engineering tasks.
By Mohit Raghavendra, Anisha Gunjal, Aakash Sabharwal, Yunzhong He
SkillForge is a self‑distillation framework that proactively builds project‑specific knowledge for large language model agents by synthesizing and resolving artificial issues derived from a repository’s test‑covered core functionalities. By distilling these solutions into entity‑grounded skills linked to repository entities, the system equips agents with reusable, project‑specific expertise before encountering real issues. Experiments with both open‑source and closed‑source models show that SkillForge consistently outperforms strong baselines in issue resolution tasks.
By Silin Chen, Han Li, Xiaodong Gu, Yuling Shi, Haibing Guan
arXiv:2607. 27146v1 Announce Type: cross Abstract: Coding agents have made substantial progress on software engineering tasks that modify existing codebases, including bug fixing and feature implementation.
By Yihao Chen, Shi Chang, Khaled Chawa, Feng Lin, Boyuan Chen, Shaowei Wang, Ahmed E. Hassan
SkillForge is a self‑distillation framework that proactively builds project‑specific knowledge for large language model agents by synthesizing and resolving artificial issues derived from a repository’s test‑covered core functionalities. Rather than waiting for real issues to reveal knowledge gaps, SkillForge generates these synthetic problems, learns reusable entity‑grounded skills, and associates them with relevant repository entities. Experiments with both open‑source and closed‑source models show that this proactive knowledge acquisition consistently outperforms strong baselines in issue resolution tasks.