Bayesian Program Analysis

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. © 2024 Chinese Institute of Electronics. All rights reserved.