Abstract
In this paper, we propose a Monte-Carlo test generation method that is able to conduct decision, condition and MC/DC coverage testing for practical Simulink models. To generate a test suite efficiently for models with dozens of thousands blocks, we introduce several techniques. Firstly, we propose using templates of input signals, which characterize shapes of entire waveforms of the signals with a few parameters. By using templates, we can easily generate candidate test cases and reduce a search space to plausible one. Secondly, we propose biased sampling framework to get efficiently test cases meeting uncovered objectives. In the framework, a biased distribution generating new candidate test cases is iteratively refined based on fitness values of the previous candidates. We performed two experiments for each of the techniques and confirmed that they are effective enough for Simulink models which cannot be dealt with a de-facto standard tool SLDV.
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Notes
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true ane false mean activated and inactivated, respectively.
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That is, the outcome is in \(\mathsf{DataPorts}(b)\) for a (multi-port) switch block b.
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For simplicity, we omit trigger conditions in this paper.
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Additionally, blocks in a subsystem with active-control ports are exercised only if the subsystem is activated. Therefore, we also need to correct a fitness for an objective related to the blocks.
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For a Boolean argument, its range is treated as [0, 1], and a value greater (resp., less) than a half is interpreted as \(\mathtt{true}\) (resp., \(\mathtt{false}\)).
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Tomita, T., Ishii, D., Murakami, T., Takeuchi, S., Aoki, T. (2019). Template-Based Monte-Carlo Test Generation for Simulink Models. In: Chamberlain, R., Taha, W., Törngren, M. (eds) Cyber Physical Systems. Design, Modeling, and Evaluation. CyPhy 2017. Lecture Notes in Computer Science(), vol 11267. Springer, Cham. https://doi.org/10.1007/978-3-030-17910-6_5
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