一句话：将探索-利用权衡引入贝叶斯程序分析，利用告警结构相关性提升交互式告警排序与消解效率。
交互式贝叶斯程序分析通过将推导建模为概率依赖，按置信度排序告警、推荐高可能告警供用户检查，并用检查结果更新置信度。但现有方法采用纯贪心的利用式策略，总检查最高置信度告警，易陷入局部最优，导致冗余检查并延迟发现真实告警。本文提出 Beer，将探索-利用权衡系统化地引入贝叶斯程序分析：利用告警间由共享根因导出的结构相关性估计期望信息增益以指导探索；当连续误报表明模型停滞时，从探索最少且高度相关的告警簇中选择告警以加速学习。Beer 在 Bingo 框架上实现，在 datarace、threadescape 和 taint 分析上相比贪心基线将排序效率最高提升 32%，验证了探索引导告警消解的有效性。
Interactive Bayesian program analysis enhances static analysis by modeling derivations as probabilistic dependencies, enabling ranking alarms by calculated confidences, proposing highly likely alarms for user inspection, and updating confidences with inspection results. Existing interactive approaches adopt a purely greedy, exploitation-only selection strategy that always inspects the highest-confidence alarm. However, such strategies are prone to local optima, leading to redundant inspections and delayed identification of true alarms. We propose B eer (Bayesian Exploration-Exploitation Ranker), a framework that systematically integrates the Exploration-Exploitation trade-off into Bayesian program analysis. B eer leverages structural correlations between alarms—derived from shared root causes in the Bayesian model—to estimate expected information gain and guide exploration. When repeated false alarms indicate model stagnation, B eer selects alarms from minimally explored, highly correlated clusters to accelerate learning. Implemented atop the B ingo framework, B eer achieves up to 32% effectiveness in ranking efficiency over the greedy baseline on datarace, threadescape, and taint analyses, demonstrating the efficacy of exploration-guided alarm resolution.
@article{paperbot3847,
title = {Beer: Interactive Alarm Resolution in Bayesian Program Analysis via Exploration-Exploitation},
author = {Haoran Lin and Zhenyu Yan and Xin Zhang},
journal = {Proceedings of the ACM on Programming Languages},
volume = {10},
number = {OOPSLA1},
year = {2026},
doi = {10.1145/3798214}
}