Automatic Generation of Simulink Models to Find Bugs in a Cyber-Physical System Tool Chain using Deep Learning.

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Abstract

Testing cyber-physical system (CPS) development tools such as MathWorks’ Simulink is very important as they are widely used in design, simulation, and verification of CPS data-flow models. Existing randomized differential testing frameworks such as SLforge leverages semi-formal Simulink specifications to guide random model generation which requires significant research and engineer- ing investment along with the need to manually update the tool, whenever MathWorks updates model validity rules. To address the limitations, we propose to learn validity rules automatically by learning a language model using our framework DeepFuzzSL from existing corpus of Simulink models. In our experiments, DeepFuzzSL consistently generate over 90% valid Simulink models and also found 2 confirmed bugs by MathWorks Support.