Generating Performance Distributions via Probabilistic Symbolic Execution

• Published in: 2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE) pp. 49 - 60
• Main Authors: Bihuan Chen, Yang Liu, Wei Le
• Format: Conference Proceeding
• Language: English
• Published: ACM 01.05.2016
• Subjects: Algorithm design and analysis; Analytical models; Computational modeling; Mathematical model; Performance analysis; Probabilistic logic; Software engineering; Symbolic Execution
• ISSN: 1558-1225
• DOI: 10.1145/2884781.2884794

Analyzing performance and understanding the potential best-case, worst-case and distribution of program execution times are very important software engineering tasks. There have been model-based and program analysis-based approaches for performance analysis. Model-based approaches rely on analytical or design models derived from mathematical theories or software architecture abstraction, which are typically coarse-grained and could be imprecise. Program analysis-based approaches collect program profiles to identify performance bottlenecks, which often fail to capture the overall program performance. In this paper, we propose a performance analysis framework PerfPlotter. It takes the program source code and usage profile as inputs and generates a performance distribution that captures the input probability distribution over execution times for the program. It heuristically explores high-probability and low-probability paths through probabilistic symbolic execution. Once a path is explored, it generates and runs a set of test inputs to model the performance of the path. Finally, it constructs the performance distribution for the program. We have implemented PerfPlotter based on the Symbolic PathFinder infrastructure, and experimentally demonstrated that PerfPlotter could accurately capture the best-case, worst-case and distribution of program execution times. We also show that performance distributions can be applied to various important tasks such as performance understanding, bug validation, and algorithm selection.