A Machine Learning approach for Statistical Software Testing

Nicolas Baskiotis 1, 2 Michèle Sebag 1, 2 Marie-Claude Gaudel 2 Sandrine-Dominique Gouraud 2
1 TANC - Algorithmic number theory for cryptology
LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau], Inria Saclay - Ile de France, X - École polytechnique, CNRS - Centre National de la Recherche Scientifique : UMR7161
Abstract : Some Statistical Software Testing approaches rely on sampling the feasible paths in the control flow graph of the program; the difficulty comes from the tiny ratio of feasible paths. This paper presents an adaptive sampling mechanism called EXIST for Exploration/eXploitation Inference for Software Testing, able to retrieve distinct feasible paths with high probability. EXIST proceeds by alternatively exploiting and updating a distribution on the set of program paths. An original representation of paths, accommodating long-range dependencies and data sparsity and based on extended Parikh maps, is proposed. Experimental validation on real-world and artificial problems demonstrates dramatic improvements compared to the state of the art.
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Communication dans un congrès
Twentieth International Joint Conference on Artificial Intelligence, Jan 2007, Hyderabad, India, 2007
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Contributeur : Nicolas Baskiotis <>
Soumis le : jeudi 9 novembre 2006 - 15:16:13
Dernière modification le : jeudi 10 mai 2018 - 02:06:59
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Nicolas Baskiotis, Michèle Sebag, Marie-Claude Gaudel, Sandrine-Dominique Gouraud. A Machine Learning approach for Statistical Software Testing. Twentieth International Joint Conference on Artificial Intelligence, Jan 2007, Hyderabad, India, 2007. 〈inria-00112681〉

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