Abstract
Model-based testing is a popular black-box testing technology that enables automation of test generation and execution, while achieving a given coverage. The application of this technology to large and complex systems is still a challenging problem, due to the state-space explosion resulting from the size of specification models.
In this paper, we evaluate a test-case generation approach that tackles this complexity along two axes. Firstly, our approach relies on a synchronous specification language for test models, thus avoiding the problem of interleaving actions. Secondly, our specification language enables incremental test-case generation by providing support for compositional modeling, in which each requirement or view of the system is expressed as a separate partial model. The individual requirement models are then naturally combined by conjunction, which is incrementally computed during the generation of tests.
We apply our test-case generation technique to two large industrial case studies: (1) an electronic control unit (ECU) of an agricultural device; and (2) a railway interlocking system. We demonstrate the scalability of our approach by creating a series of test models with increasing complexity and report on the experimental results.
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Aichernig, B.K., Ničković, D., Tiran, S. (2015). Scalable Incremental Test-case Generation from Large Behavior Models. In: Blanchette, J., Kosmatov, N. (eds) Tests and Proofs. TAP 2015. Lecture Notes in Computer Science(), vol 9154. Springer, Cham. https://doi.org/10.1007/978-3-319-21215-9_1
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DOI: https://doi.org/10.1007/978-3-319-21215-9_1
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