Smoke Test Planning using Answer Set Programming
Smoke testing is an important method to increase stability and reliability of hardware- gramming, Testing depending systems. Due to concurrent access to the same physical resource and the impracticality of the use of virtualization, smoke testing requires some form of planning. In this paper, we propose to decompose test cases in terms of atomic actions consisting of preconditions and effects. We present a solution based on answer set programming with multi-shot solving that automatically generates short parallel test plans. Experiments suggest that the approach is feasible for non-inherently sequential test cases and scales up to thousands of test cases.
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International Journal of Interactive Multimedia and Artificial Intelligence
Special Issue on Artificial Intelligence, Paving the Way to the Future
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