PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Machine Learning approach for Statistical Software Testing
Nicolas Baskiotis, Michèle Sebag, Marie-Claude Gaudel and Sandrine Gouraud
In: Twentieth International Joint Conference on Artificial Intelligence, 6-12 Jan 2007, Hyderabad, India.


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.

EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:2508
Deposited By:Nicolas Baskiotis
Deposited On:22 November 2006