一句话：提出两阶段弱监督框架 ACWRecommender，从静态分析告警中推荐高概率为真实缺陷的可操作警告。
静态分析工具误报率可高达90%，阻碍开发者采用。现有可操作警告的收集假设不准确，会产生大量无效警告。本文从Top-500 GitHub C仓库的68,274次reversion中构建首个大规模可操作警告数据集，并为每条警告赋予其成为真实缺陷可能性的弱标签。作者提出两阶段框架ACWRecommender：先以可操作警告识别任务预热UniXcoder进行粗粒度检测，再以弱监督学习对高概率真实缺陷的可操作警告（AWHB）重排。实验显示其在nDCG与MRR上大幅优于基线；在6个项目上人工检查2,197条告警，推荐top-10中27条被开发者确认真实bug。
The use of static analysis tools has gained increasing popularity among developers in the last few years. However, the widespread adoption of static analysis tools is hindered by their high false alarm rates (up to 90%). Previous studies have introduced the concept of actionable warnings and built machine-learning method to distinguish actionable warnings from false alarms. However, according to our empirical observation, the current assumption used for actionable warning(s) collection is rather shaky and inaccurate, leading to a large number of invalid actionable warnings. To address this problem, in this study, we build the first large actionable warning dataset by mining 68,274 reversions from Top-500 GitHub C repositories, we then take one step further by assigning each actionable warning a weak label regarding its likelihood of being a real bug. Following that, we propose a two-stage framework called ACWRecommender to automatically recommend the actionable warnings with high probability to be real bugs (AWHB). Our approach warms up the pre-trained model UniXcoder by identifying actionable warnings task (coarse-grained detection stage) and rerank AWHB to the top by weakly supervised learning (fine-grained reranking stage). Experimental results show that our proposed model outperforms several baselines by a large margin in terms of nDCG and MRR for AWHB recommendation. Moreover, we ran our tool on 6 randomly selected projects and manually checked the top-ranked warnings from 2,197 reported warnings, we reported top-10 recommended warnings to developers, 27 of them were already confirmed by developers as real bugs. Developers can quickly find real bugs among the massive amount of reported warnings, which verifies the practical usage of our tool.
@inproceedings{abs-2511-12229,
title = {Actionable Warning Is Not Enough: Recommending Valid Actionable Warnings with Weak Supervision},
author = {Zhipeng Xue and Zhipeng Gao and Tongtong Xu and Xing Hu and Xin Xia and Shanping Li},
booktitle = {ICSE},
year = {2026},
doi = {10.1145/3744916.3773133}
}