Bahareh Badban
ABSTRACT
This article presents novel results on automated test generation for hybrid control systems, which involves the generation of both discrete and real-valued, potentially time-continuous, input data to the system under test. Our generation techniques are allocated in two layers: The upper layer contains a symbolic test case generator constructing test cases as paths through an abstracted representation model of the system under test. Different test strategies designed to pursue various quality objectives lead to different selections of symbolic test cases. Symbolic test cases are transformed into feasible, i. e., executable, test cases by constructing concrete sequences of input data, allowing the execution of the pre-planned transition sequence. The input data construction is performed by the lower layer consisting of a constraint solver which applies interval analysis techniques to identify the domains from where to pick the appropriate test data. This process is made efficient by combining subpaving with forward-backward interval constraint propagation. On both layers learning algorithms are applied in order to avoid the spending of computation time on paths and sub-constraints, respectively, which are already known not to contribute to the solution.
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Index Terms
- Test automation for hybrid systems
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