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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
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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Submitted on : Thursday, November 9, 2006 - 3:16:13 PM
Last modification on : Sunday, June 26, 2022 - 11:46:25 AM
Long-term archiving on: : Tuesday, April 6, 2010 - 7:19:20 PM


  • HAL Id : inria-00112681, version 1



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. ⟨inria-00112681⟩



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