CPN simulation-based test case generation from controlled natural-language requirements

Abstract We propose a test generation strategy from natural language (NL) requirements via translation into Coloured Petri Nets (CPN), an extension of Petri Nets that supports model structuring and provides a mature theory and powerful tool support. This approach extends our previous work on the NAT2TEST framework, which involves syntactic and semantic analyses of NL requirements and the generation of Data-Flow Reactive Systems (DFRS) as an intermediate representation, from which target formal models can be obtained for the purpose of test case generation. Our contributions include automating a systematic translation of DFRSs into CPN models, an extension to deal with time aspects, besides an empirical analysis of the CPN-based test generation strategy. The analyses considered examples both from the literature (a vending machine and a nuclear power plant control system), and from the aerospace and the automotive domain (a priority command control system and a turn indicator control system, respectively). We analysed performance and the ability to detect defects generated via mutation. The results provide evidence that the contribution proposed here is more efficient, besides being able to detect at least as many defects as our previous efforts.