Structural Sampling for Statistical Software Testing

Authors Nicolas Baskiotis, Michele Sebag

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Nicolas Baskiotis
Michele Sebag

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Nicolas Baskiotis and Michele Sebag. Structural Sampling for Statistical Software Testing. In Probabilistic, Logical and Relational Learning - A Further Synthesis. Dagstuhl Seminar Proceedings, Volume 7161, pp. 1-13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2008)


Structural Statistical Software Testing exploits the control flow graph of the program being tested to construct test cases. While test cases can easily be extracted from {em feasible paths} in the control flow graph, that is, paths which are actually exerted for some values of the program input, the feasible path region is a tiny fraction of the graph paths (less than $10^{-5}]$ for medium size programs). The S4T algorithm presented in this paper aims to address this limitation; as an Active Relational Learning Algorithm, it uses the few feasible paths initially available to sample new feasible paths. The difficulty comes from the non-Markovian nature of the feasible path concept, due to the long-range dependencies between the nodes in the control flow graph. Experimental validation on real-world and artificial problems demonstrates significant improvements compared to the state of the art.
  • Active Relational Learning
  • Software Testing
  • Autonomic Computing
  • Parikh Maps


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