arXiv:2607. 28591v1 Announce Type: cross Abstract: Scaling coding agents requires a continuing supply of executable data for training, benchmarking, and continuous evaluation.
By Haomin Qi, Xingliang Wang, Xuanqi Gao, Baihui Sang, Xin Zhang, Minghua Ma, Pengfei Gao, Yu Kang, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang
Scaling coding agents requires a continuing supply of executable data for training, benchmarking, and continuous evaluation. Each task must couple a realistic software state with a specification, development tools, and reliable verification.
The paper introduces ORACLE‑SWE, a study that systematically evaluates how different intermediate information signals—such as Reproduction Test, Regression Test, Edit Location, Execution Context, and API Usage—contribute to the success of language‑model agents on software engineering tasks. It quantifies the impact of both oracle‑provided signals and those extracted by agents, and measures the performance gap between current agent‑found signals and their oracle counterparts. The authors release code and a dataset to support future signal‑analysis research in this area.
By Kenan Li, Qirui Jin, Liao Zhu, Xiaosong Huang, Yijia Wu, Yikai Zhang, Xin Zhang, Zijian Jin, Yufan Huang, Elsie Nallipogu, Chaoyun Zhang, Yu Kang, Saravan Rajmohan, Qingwei Lin, Wenke Lee, Dongmei Zhang
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.
By Yunxiang Wei, Zhenyu Lei, Jundong Li
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.
By Jiale Amber Wang, Kaiyuan Wang, Pengyu Nie
arXiv:2605. 02411v2 Announce Type: replace Abstract: A semantic gap separates how users describe tasks from how tools are documented.
By Kyle Zheng, Han Zhang, Renliang Sun, Chenchen Ye, Wei Wang