Learning-based testing of automotive ECUs

Abstract: LBTest is a learning based-testing tool for black box testing, developed by the software reliability group at KTH. Learning based-testing combines model checking with a learning algorithm that incrementally learns a model of the system under test, which allows for a high degree of automation. This thesis examines the possibilities to use LBTest for testing of electronic control units (ECUs) at Scania. Through two case studies the possibility to formalise ECU requirements and to model ECU applicationsfor LBTest are evaluated. The case studies are followed up with benchmarking against test cases currently in use at Scania. The results of the case studies show that most of the functional requirements can, after reformulation, be formalised for LBTest and that LBTest can find previously undetected defects in ECU software. The benchmarking also shows a high error detection rate for LBTest. Finally, the thesis presents guidelines for requirement formulation and improvements of LBTest are suggested.