Partition Based Differential Testing for Finding Embedded Code Generation Bugs in Simulink
He Jiang, Hongyi Cheng, Shikai Guo, Xiaochen Li
Abstract
Engineers frequently generate embedded code from Simulink models for control applications. However, target applications using the code could behave unexpectedly, due to the bugs in code generation. In this study, we propose MOPART, the first model partition based differential testing method for code generation testing in Simulink. MOPART uses multiple-way network partitioning to generate diverse bug-triggering Simulink models to thoroughly exercise the code generation process. MOPART then finds bugs by analyzing the outputs of these Simulink models with differential testing. Experiments show that MOPART significantly outperforms existing approaches, which finds 11 confirmed code generation bugs in only two weeks.
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Builds on3
- SLEMI: equivalence modulo input (EMI) based mutation of CPS models for finding compiler bugs in SimulinkShafiul Azam Chowdhury, Sohil Lal Shrestha, Taylor T. Johnson, Christoph CsallnerICSE 2020 · 35 citations
- HDTest: Differential Fuzz Testing of Brain-Inspired Hyperdimensional ComputingDongning Ma, Jianmin Guo, Yu Jiang, Xun JiaoDAC 2021 · 27 citations
- HCG: optimizing embedded code generation of simulink with SIMD instruction synthesisZhuo Su, Zehong Yu, Dongyan Wang, Yixiao Yang et al.DAC 2022 · 10 citations
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