A Machine Learning Approach for Statistical Software Testing
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 2274-2278.
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.
@inproceedings{ijcai2007p366,
title = {A Machine Learning Approach for Statistical Software Testing},
author = {Baskiotis, Nicolas and Sebag, Michèle and Gaudel, Marie-Claude and Gouraud, Sandrine-Dominique},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {2274--2278},
year = {2007},
url = {https://ijcai.org/proceedings/2007/366},
}
TY - CPAPER TI - A Machine Learning Approach for Statistical Software Testing AU - Baskiotis, Nicolas AU - Sebag, Michèle AU - Gaudel, Marie-Claude AU - Gouraud, Sandrine-Dominique T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 2274 EP - 2278 UR - https://ijcai.org/proceedings/2007/366 ER -Copied
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