Applying selective mutation strategies to the AsmetaL language

Abstract

Abstract state machines (ASMs) have been introduced as a computation model that is more powerful and more universal than standard computation models. The early validation of ASM models would help reduce the cost and risk of having defects propagate, through refinement, to other models, and eventually to code; thus, adversely affecting the quality of the end product. Mutation testing is a well-established fault-based technique for assessing and improving the quality of test suites. However, little research has been devoted to mutation analysis in the context of ASMs. Mutation testing is known to be computationally expensive due to the large number of generated mutants that are executed against a test set. In this study, the authors empirically investigate the application of cost reduction strategies to AsmetaL, an ASM-based formal language. Furthermore, they evaluate experimentally the effectiveness and the savings resulting from applying two techniques: namely, random mutants selection and operator-based selective mutation, in the context of the AsmetaL language. The quantitative results show that both techniques achieved good savings without major impact on effectiveness.