电子学报2024，Vol.52Issue(4)：1155-1172,18.DOI:10.12263/DZXB.20230973
贝叶斯程序分析
Bayesian Program Analysis
摘要
Abstract
Program analysis plays a critical role in software development and maintenance.However,traditional log-ic-based program analysis methods exhibit significant limitations when dealing with modern,complex,large-scale,and dy-namically 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 tech-niques combine probability information with traditional logic analysis to capture the uncertainty inherent in software sys-tems.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.Bayes-ian program analysis employs probabilistic logic programming to simultaneously handle both probability and logic informa-tion,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
程序分析/逻辑编程/概率逻辑编程/贝叶斯网络/贝叶斯推断Key words
program analysis/logic programming/probabilistic logic programming/bayesian network/bayesian in-ference分类
信息技术与安全科学引用本文复制引用
张昕,王冠成,吴宜谦,陈逸凡,李天驰,张羿凡,熊英飞..贝叶斯程序分析[J].电子学报,2024,52(4):1155-1172,18.基金项目
国家重点研发计划(No.2022YFB4501902) （No.2022YFB4501902）
国家自然科学基金(No.62172017) National Key Research and Development Program of China(No.2022YFB4501902) （No.62172017）
National Natural Science Foundation of China(No.62172017) （No.62172017）
