Machine learning based test data generation for safety-critical software

Unit testing focused on Modified Condition/Decision Coverage (MC/DC) criterion is essential in development safety-critical systems. However, design of test data that meets the MC/DC criterion currently needs detailed manual analysis of branching conditions in units under test by test engineers. Multiple state-of-art approaches exist with proven usage even in industrial projects. However, these approaches have multiple shortcomings, one of them being the Path explosion problem which has not been fully solved yet. Machine learning methods as meta-heuristic approximations can model behaviour of programs that are hard to test using traditional approaches, where the Path explosion problem does occur and thus could solve the limitations of the current state-of-art approaches. I believe, motivated by an ongoing collaboration with an industrial partner, that the machine learning methods could be combined with existing approaches to produce an approach suitable for testing of safety-critical projects.