一句话：提出细粒度分析，使演化感知运行时验证更精确地只重监控受代码改动影响的规约。
运行时验证（RV）通过让测试对照形式规约来发现缺陷，但在持续集成中往往过慢。演化感知技术据此提出只重监控受代码改动影响的规约子集以加速 RV，然而现有技术依赖类级粗粒度分析，会不必要且不精确地重监控未受影响的规约。本文提出细粒度分析，旨在更精确地识别受改动影响的规约，减少冗余重监控，从而提升演化感知 RV 的精度与效率。摘要未报告具体实验评估与量化结果。
Runtime verification (RV) found many bugs by monitoring passing tests in many open-source projects against formal specifications (specs). But, RV is often too slow for use in continuous integration. So, evolution-aware techniques were proposed to speed up RV by re-monitoring only a subset of specs affected by code changes. These techniques use coarse-grained class-level analyses, so they can sub-optimally and imprecisely re-monitor unaffected specs.
@inproceedings{paperbot11522,
title = {Fine-Grained Analyses for Evolution-Aware Runtime Verification},
author = {Pengyue Jiang and Kevin Guan and Mahdi Khosravi and Moustafa Ismail and Marcelo d'Amorim and Owolabi Legunsen},
booktitle = {ICSE},
year = {2026},
doi = {10.1145/3744916.3787833}
}