Live Region Mutation Testing for Commercial Cyber-Physical System Development Tool Chain

MathWorks Simulink, a commercial CPS development tool chain, is widely used as an industry standard for designing and analyzing system behavior and generating embedded code for deployment. However, bugs in Simulink can cause unexpected behaviors during model compilation, making their elimination critical. Existing methods face two key challenges: generating equivalent models with varied data flows (data flow equivalence) and creating diverse block types to comprehensively test the compiler (mutation diversity). To address these, we propose LION, a differential testing approach. LION ensures data flow equivalence by inserting “store-revert” block pairs between existing blocks and tackles mutation diversity by employing Markov Chain Monte Carlo (MCMC) sampling to generate diverse new blocks. Differential testing is then used to identify bugs. Experiments show LION outperforms state-of-the-art approaches like SLforge, SLEMI, and COMBAT, detecting 610 additional compiler bugs in two weeks. Over two months, LION uncovered and reported 16 valid bugs in the widely used stable version of Simulink.

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Publication Details

Published
2025-06-22
DOI
https://doi.org/10.1109/dac63849.2025.11133188
Primary Topic
Scientific Computing and Data Management
Type
article
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article

Live Region Mutation Testing for Commercial Cyber-Physical System Development Tool Chain

He Jiang, Shikai Guo, Xiaochen Li, Xiaoyu Wang et al.
Scientific Computing and Data Management
article

Live Region Mutation Testing for Commercial Cyber-Physical System Development Tool Chain

He Jiang, Shikai Guo, Xiaochen Li, Xiaoyu Wang, Zixuan Wang, Lehuan Zhang
article en

Abstract

MathWorks Simulink, a commercial CPS development tool chain, is widely used as an industry standard for designing and analyzing system behavior and generating embedded code for deployment. However, bugs in Simulink can cause unexpected behaviors during model compilation, making their elimination critical. Existing methods face two key challenges: generating equivalent models with varied data flows (data flow equivalence) and creating diverse block types to comprehensively test the compiler (mutation diversity). To address these, we propose LION, a differential testing approach. LION ensures data flow equivalence by inserting “store-revert” block pairs between existing blocks and tackles mutation diversity by employing Markov Chain Monte Carlo (MCMC) sampling to generate diverse new blocks. Differential testing is then used to identify bugs. Experiments show LION outperforms state-of-the-art approaches like SLforge, SLEMI, and COMBAT, detecting 610 additional compiler bugs in two weeks. Over two months, LION uncovered and reported 16 valid bugs in the widely used stable version of Simulink.

Dalian University of Technology (CN), Dalian Maritime University (CN)
Openalex Percentile: Top 76%
Scientific Computing and Data Management
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