PatchBench introduces a benchmark to evaluate AI agents on realistic vulnerability patching tasks, addressing two key threats to validity: patch memorization and surface-level fixes that merely suppress crashes. The study finds that 25% of agent patches resemble historical developer patches, and that PoC-only validation inflates success rates by 1.83× on average. PatchBench mitigates these issues by selecting vulnerabilities whose true fixes lie outside the crash stack, migrating historical vulnerabilities into new contexts, and employing rigorous validation for security and semantic correctness.
By Chihao Shen, Jiacheng Li, Aastha Mahajan, Jeffery Siyuan Tian, Yonghwi Kwon, Yizheng Chen
arXiv:2607. 12605v1 Announce Type: cross Abstract: Large language models (LLMs) have improved automated program repair (APR), but two limitations remain.
By Zhili Huang, Ling Xu, Hongyu Zhang
arXiv:2607. 00990v1 Announce Type: cross Abstract: Large language model (LLM)-based software engineering agents are increasingly developed to resolve software issues by generating patches from issue reports and code repositories.
By Yaoqi Guo, Yang Liu, Jie M. Zhang, Yun Ma, Yiling Lou, Zhenpeng Chen
The paper introduces Porting Benchmark, a dataset of 1,234 security patch backporting cases covering cross-version, cross-branch, and cross-repository scenarios, along with a unified evaluation framework. Five tools—spanning program analysis, LLM prompting, and LLM agents—are evaluated under aligned settings, revealing that performance varies significantly across tools and patch complexity, with success rates dropping sharply for structurally complex patches. The study identifies four root-cause categories for failures and demonstrates that reference-based benchmark scores may not fully reflect real-world remediation, as executable validation uncovers additional integration issues.
The paper introduces Porting Benchmark, a curated dataset of 1,234 security patch backporting cases that span cross-version, cross-branch, and cross-repository scenarios, along with a common evaluation framework. Five tools—spanning program analysis, LLM prompting, and LLM agents—are evaluated under aligned settings, revealing that performance varies significantly across tools and that complex patches (Type-IV) see a sharp drop in success rate. The study identifies four root-cause categories for failures and demonstrates that reference-based benchmark scores may not fully capture real-world remediation, as executable validation uncovers additional integration issues.
By Jincheng Yang, Yulong Fu, Chengwei Liu, Lyuye Zhang, Fangyuan Zhang, Bingyang Ren, Yang Liu, Hui Li
The paper introduces THEMIS, a stage-aware repair workflow that externalizes the requirement-to-repair process by generating semantic interpretations, a runtime requirement-code graph, graph-derived developer guidance, retained repair rationale and patches, and post-edit audit records. A retrospective audit of 300 SWE-bench Lite cases shows that these artifacts enable cross-stage inspection, with a complete developer rationale available for 288 cases and 214 cases retaining a full audited field set. The retained records also allow systematic measurement of cross-stage correspondence, revealing high recurrence of target symbols across rationales and patches, and a preliminary improvement in resolving cases compared to a direct same-input condition.
By Zewen Tao, Shin-nosuke Ishikawa