Bayesian Program Analysis

ZHANG Xin; WANG Guan-cheng; WU Yi-qian; CHEN Yi-fan; LI Tian-chi; ZHANG Yi-fan; XIONG Ying-fei

doi:10.12263/DZXB.20230973

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Bayesian Program Analysis

Special Issue: Recipients of CIE Science and Technology Awards | 更新时间：2025-12-08

- Bayesian Program Analysis
- ACTA ELECTRONICA SINICA Vol. 52, Issue 4, Pages: 1155-1172(2024)
- 作者机构：
  
  1.高可信软件技术教育部重点实验室（北京大学），北京 100871
  2.北京大学计算机学院，北京 100871
- 作者简介：
- 基金信息：
  
  National Key Research and Development Program of China(2022YFB4501902);National Natural Science Foundation of China(62172017)
- DOI：10.12263/DZXB.20230973
  CLC： TP312;
- Received：19 October 2023，
  
  Revised：2024-02-09，
  
  Published：25 April 2024
- 稿件说明：
移动端阅览
张昕,王冠成,吴宜谦,等. 贝叶斯程序分析[J]. 电子学报,2024,52(04):1155-1172.

ZHANG Xin, WANG Guan-cheng, WU Yi-qian, et al. Bayesian Program Analysis[J]. Acta Electronica Sinica, 2024, 52(04): 1155-1172.
张昕,王冠成,吴宜谦,等. 贝叶斯程序分析[J]. 电子学报,2024,52(04):1155-1172. DOI：10.12263/DZXB.20230973

ZHANG Xin, WANG Guan-cheng, WU Yi-qian, et al. Bayesian Program Analysis[J]. Acta Electronica Sinica, 2024, 52(04): 1155-1172. DOI：10.12263/DZXB.20230973

摘要

程序分析在软件开发和维护中发挥着关键作用.然而，传统基于逻辑的程序分析方法在处理现代复杂、大规模和动态特性丰富的软件系统时往往效果有限，其根源在于软件系统中的不确定性.研究人员针对具体的程序分析问题提出了一系列新的技术，其特征是在传统逻辑分析的基础上结合概率信息来捕获软件系统中的不确定性.通过总结和抽象这些已有工作，本文提出了贝叶斯程序分析框架，其核心思想是结合程序分析和贝叶斯统计推断，通过建模和更新关于程序的概率分布来推断有关程序行为的信息.贝叶斯程序分析采用概率逻辑编程来同时处理概率信息和逻辑信息，用统一的方式捕获了现有的多项不同工作，也能泛化到程序缺陷定位和差异调试等非传统程序静态分析任务上.本文给出了贝叶斯程序分析框架的定义，展示了该框架在程序分析和相关领域的应用，并展望了未来发展方向.

Abstract

Program analysis plays a critical role in software development and maintenance. However

traditional logic-based program analysis methods exhibit significant limitations when dealing with modern

complex

large-scale

and dynamically rich software systems. The root cause of these limitations lies in the uncertainty present in software systems. To address this issue

researchers have proposed a series of new techniques for specific program analysis problems. These techniques combine probability information with traditional logic analysis to capture the uncertainty inherent in software systems. By summarizing and abstracting existing work in this area

this paper introduces the Bayesian program analysis framework. The core idea of this framework is to integrate program analysis with Bayesian statistical inference. It does so by modeling and updating probability distributions about the program to infer information about program behavior. Bayesian program analysis employs probabilistic logic programming to simultaneously handle both probability and logic information

providing a unified approach that encompasses various existing works. It can also be generalized to non-traditional static program analysis tasks

such as program fault localization and delta debugging. This paper provides a definition of the Bayesian program analysis framework

demonstrates its applications in program analysis and related fields

and outlines future directions for development.

关键词

Keywords

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