ML Anthology

A Machine Learning Approach for Statistical Software Testing

Nicolas Baskiotis, Michèle Sebag, Marie-Claude Gaudel, Sandrine-Dominique Gouraud
IJCAI 2007 pp. 2274-2279
/ijcai/2007/baskiotis2007ijcai-machine/

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. URL: http://www.lri.fr/~nbaskiot/papier/existIJ07.pdf

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Text

Baskiotis et al. "A Machine Learning Approach for Statistical Software Testing." International Joint Conference on Artificial Intelligence, 2007.

Markdown

[Baskiotis et al. "A Machine Learning Approach for Statistical Software Testing." International Joint Conference on Artificial Intelligence, 2007.](https://mlanthology.org/ijcai/2007/baskiotis2007ijcai-machine/)

BibTeX

@inproceedings{baskiotis2007ijcai-machine,
  title     = {{A Machine Learning Approach for Statistical Software Testing}},
  author    = {Baskiotis, Nicolas and Sebag, Michèle and Gaudel, Marie-Claude and Gouraud, Sandrine-Dominique},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2007},
  pages     = {2274-2279},
  url       = {https://mlanthology.org/ijcai/2007/baskiotis2007ijcai-machine/}
}