A novel approach to HW/SW integration testing of route-based interlocking system controllers

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Abstract

Recent progress in bounded model checking and inductive reasoning has shown that the fully automated verification of route-based interlocking system designs of realistic “real-world” complexity is possible and ready for industrial application. In this paper, we present a new model-based testing strategy for interlocking system controllers that exploits the fact that the design has already been verified, so that it can be used as a reference model for test case and test oracle generation. Our special interest lies in the field of complete testing strategies that are able to uncover every implementation error, provided that the implementation behaviour is captured in a pre-specified fault domain. Despite their guaranteed test strength, these strategies have two well-known disadvantages: (1) applied in a naive way, they often result in an infeasible amount of test cases, and (2) the hypothesis that the real implementation behaviour is captured by a member of the fault domain can rarely be justified in a convincing way. We describe a new combination of compositional reasoning and input equivalence class generation techniques that removes problem (1). For coping with disadvantage (2), we suggest a combination of equivalence class and random testing that while not being able to guarantee complete fault coverage for implementations outside the fault domain results in a test strength that is significantly higher than heuristic test approaches for interlocking system controllers. Estimates are presented that show how application of this novel strategy reduces the effort for HW/SW integration testing, while simultaneously increasing the fault coverage in comparison to more conventional testing approaches.

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Peleska, J., Huang, W. L., & Hübner, F. (2016). A novel approach to HW/SW integration testing of route-based interlocking system controllers. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9707, pp. 32–49). Springer Verlag. https://doi.org/10.1007/978-3-319-33951-1_3

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