Search-Based Automated Testing of Continuous Controllers: Framework, Tool Support, and Case Studies

peer reviewedContext. Testing and verification of automotive embedded software is a major chal- lenge. Software production in automotive domain comprises three stages: Developing automotive functions as Simulink models, generating code from the models, and de- ploying the resulting code on hardware devices. Automotive software artifacts are sub- ject to three rounds of testing corresponding to the three production stages: Model-in- the-Loop (MiL), Software-in-the-Loop (SiL) and Hardware-in-the-Loop (HiL) testing. Objective. We study testing of continuous controllers at the Model-in-Loop (MiL) level where both the controller and the environment are represented by models and connected in a closed loop system. These controllers make up a large part of automotive functions, and monitor and control the operating conditions of physical devices. Method. We identify a set of requirements characterizing the behavior of continu- ous controllers, and develop a search-based technique based on random search, adap- tive random search, hill climbing and simulated annealing algorithms to automatically identify worst-case test scenarios which are utilized to generate test cases for these requirements. Results. We evaluated our approach by applying it to an industrial automotive con- troller (with 443 Simulink blocks) and to a publicly available controller (with 21 Simulink blocks). Our experience shows that automatically generated test cases lead to MiL level simulations indicating potential violations of the system requirements. Further, not only does our approach generate significantly better test cases faster than random test case generation, but it also achieves better results than test scenarios devised by domain experts. Finally, our generated test cases uncover discrepancies between envi- ronment models and the real world when they are applied at the Hardware-in-the-Loop(HiL) level. Conclusion. We propose an automated approach to MiL testing of continuous con- trollers using search. The approach is implemented in a tool and has been successfully applied to a real case study from the automotive domain