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Finding bugs in a commercial cyber-physical system (CPS) development toolsuch as Simulink is hard as its codebase contains millions of lines of code andcomplete formal language specifications are not available. While deep learningtechniques promise to learn such language specifications from sample models,deep learning needs a large number of training data to work well. SLGPTaddresses this problem by using transfer learning to leverage the powerfulGenerative Pre-trained Transformer 2 (GPT-2) model, which has been pre-trainedon a large set of training data. SLGPT adapts GPT-2 to Simulink with bothrandomly generated models and models mined from open-source repositories. SLGPTproduced Simulink models that are both more similar to open-source models thanits closest competitor, DeepFuzzSL, and found a super-set of the Simulinkdevelopment toolchain bugs found by DeepFuzzSL.

SLGPT: Using Transfer Learning to Directly Generate Simulink Model Files and Find Bugs in the Simulink Toolchain