Automatic Generation of Simulink Models to Find Bugs in Cyber-Physical System Tool Chain using Deep Learning
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 engineering 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.
Wed 8 JulDisplayed time zone: (UTC) Coordinated Universal Time change
17:10 - 18:00 | |||
17:10 50mPoster | Improving Bug Detection and Fixing via Code Representation Learning ACM Student Research Competition Yi Li New Jersey Institute of Technology, USA | ||
17:10 50mPoster | Automatic Generation of Simulink Models to Find Bugs in Cyber-Physical System Tool Chain using Deep Learning ACM Student Research Competition Sohil Lal Shrestha The University of Texas at Arlington DOI Pre-print | ||
17:10 50mPoster | Studying and Suggesting Logging Locations in Code Blocks ACM Student Research Competition Zhenhao Li Concordia University | ||
17:10 50mPoster | An Automated Framework For Gaming Platform To Test Multiple Games ACM Student Research Competition Zihe Song The University of Texas at Dallas | ||
17:10 50mPoster | Efficient test execution in End to End testing ACM Student Research Competition Cristian Augusto University of Oviedo | ||
17:10 50mPoster | An Empirical Study on the Evolution of Test Smell ACM Student Research Competition Dong Jae Kim Concordia University | ||
