Improving Model-Based Testing in Automotive Software Engineering

Abstract

Testing is crucial to successfully engineering reliable automotive software. The manual derivation of test cases from ambiguous textual requirements is costly and error-prone. Model-based development can reduce the test case derivation effort by capturing requirements in structured models from which test cases can be generated with reduced effort. To facilitate the automated test case derivation at BMW, we conducted an anonymous survey among its testing practitioners and conceived a model-based improvement of the testing activities. The new model-based test case derivation extends BMW's SMArDT method with automated generation of tests, which addresses many of the practitioners' challenges uncovered through our study. This ultimately can facilitate quality assurance for automotive software.