Large Language Models (LLMs) are evolving from simple code completion tools to repository‑scale agents capable of retrieving context, editing files, executing tools, and engaging in security‑sensitive workflows. A structured survey up to May 31 2026 reviews LLM work across software engineering and security tasks, adaptation mechanisms, artifact granularity, and evaluation design, and introduces an assurance framework that separates functional correctness, security, operational reliability, evidence provenance, and agent authority. The review highlights that while execution feedback and repository access improve engineering task completion, they do not guarantee security, and static‑analysis labels rarely ensure deployable correctness; it also identifies common validity threats and proposes a minimum reporting protocol and a research agenda focused on jointly secure‑and‑functional benchmarks, repository‑scale threat models, calibrated human oversight, longitudinal maintainability evidence, and reproducible agent evaluation.
By Wei Lin, Tao Zhou, Zhaofei Xie, Changgui Hong
The paper introduces SWE Refactor Bench, a benchmark of 20 whole‑repository migrations aimed at testing whether coding agents can autonomously perform long‑horizon stack migrations. It employs a three‑stage evaluation—Migration Audit, Behavioural Tests, and Agentic Verification—to assess both migration completeness and behavioural correctness, revealing that only 5.4 % of runs succeed across all stages. The study shows that agents struggle with migration tasks, especially language rewrites, and highlights the need for more robust coding‑agent capabilities.
By Deyao Hong, Yizhe Chi, Wenyi Li, Xiaoqiu Wang, Mingju Gao, Kaisen Yang, Bingxiang He, Youjie Zheng, Calvin Xiao, Qinhuai Na
SWE‑Gate is a new repository‑level benchmark that evaluates software engineering agents on both functional correctness and review‑derived acceptance constraints. It creates 303 repair instances from real pull‑request review comments across 75 open‑source Python projects, providing separate functional and constraint tests along with compliant and non‑compliant patches. Experiments with four LLM backends show that while 644 repairs pass functional tests, 221 fail to meet the review constraints, highlighting a gap between functional success and full repair compliance.
By Xin He, Yanlin Wang, Mingwei Liu, Jiachi Chen, Hongyu Zhang, Guanbin Li
arXiv:2607. 03691v2 Announce Type: replace-cross Abstract: Coding agents, autonomous systems that use large language models (LLMs) to resolve software engineering tasks, rely on agent harness: a middleware layer in between a developer and a large language model that orchestrates system prompts, tool execution, context management, and iterative reasoning loops.
By Oussama Ben Sghaier, Hao Li, Bram Adams, Ahmed E. Hassan
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:2609.38269v1 Announce Type: cross
Abstract: Coding agents are increasingly asked to build software rather than patch it, yet benchmarks for from-scratch repository construction are mostly limit...
By Pei Yang, Tianyu Shi, Yuhang Yao, Wanyi Chen, Tongyun Yang, Dun Pei, Haonan Wang, Pengbin Feng, Guanxu Yu, Jingchun Huang, Zeyu Zhang, Shuhan Sun, Hao Li, Xiang Li, Jie Xiao, Xinyu Wang, Hanxin Chen, Daqi Li, Qi Jia, Hongshan Lin, Zhizhou Gu, Zijun Tian, Weizhi Du, Lynn Ai, Eric Yang