Learning Abstraction Selection for Bayesian Program Analysis

Yifan Zhang, Yuanfeng Shi,Xin Zhang

Proceedings of the ACM on Programming Languages (PACMPL)（2024）

Cited 0|Views10

Abstract

We propose a learning-based approach to select abstractions for Bayesian program analysis. Bayesian program analysis converts a program analysis into a Bayesian model by attaching probabilities to analysis rules. It computes probabilities of analysis results and can update them by learning from user feedback, test runs, and other information. Its abstraction heavily affects how well it learns from such information. There exists a long line of works in selecting abstractions for conventional program analysis but they are not effective for Bayesian program analysis. This is because they do not optimize for generalization ability. We propose a data-driven framework to solve this problem by learning from labeled programs. Starting from an abstraction, it decides how to change the abstraction based on analysis derivations. To be general, it considers graph properties of analysis derivations; to be effective, it considers the derivations before and after changing the abstraction. We demonstrate the effectiveness of our approach using a datarace analysis and a thread-escape analysis.

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Key words

Static analysis,Bayesian network,alarm ranking,machine learning for program analysis,abstract interpretation

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【要点】：本文提出一种基于学习的方法为贝叶斯程序分析选择抽象，该方法优化了抽象选择以提高泛化能力，创新点在于将数据驱动的框架应用于标注的程序以改进贝叶斯程序分析。

【方法】：该方法基于数据分析，从已标注的程序中学习，以决定如何改变当前的抽象。

【实验】：作者使用数据竞争分析和线程逃逸分析来验证其方法的有效性，实验结果显示该方法能够提高贝叶斯程序分析的性能。