Combinatorial testing typically considers a single input model and creates a single test set that achieves$t$-way coverage. This paper addresses the problem of combinatorial test generation for multiple input models with shared parameters. We formally define the problem and propose an efficient approach to generating multiple test sets, one for each input model, that together satisfy$t$-way coverage for all of these input models while minimizing the amount of redundancy between these test sets. We report an experimental evaluation that applies our approach to five real-world applications. The results show that our approach can significantly reduce the amount of redundancy between the test sets generated for multiple input models and perform better than a post-optimization approach.

Model-based testing (MBT) generates tests from behavioral models of systems. When applying MBT to safety-critical systems, one problem is that textual requirements from which the behavior model is generated focus on commonly used scenarios while missing other scenarios that may lead to hazards. We propose to combine MBT with a hazard analysis technique, Hazard and Operability analysis. We first derive guide phrases from original requirements, and use these phrases to extend original requirements by adding more alternative scenarios. Second, we create timed automata from the extended requirements. Third, we validate the automata with model checking. We report a case study where our approach was applied to train control system. We created two groups of automata from original and extended requirements, respectively. We found that the automata created from extended requirements are more likely to avoid problems such as deadlock. Furthermore, tests generated from such models cover more system behaviors.