---
title: "A Machine Learning Approach for Statistical Software Testing"
authors:
  - "Nicolas Baskiotis"
  - "Michèle Sebag"
  - "Marie-Claude Gaudel"
  - "Sandrine-Dominique Gouraud"
doi: ""
url: "https://ijcai.org/proceedings/2007/366"
pdf: "https://ijcai.org/proceedings/2007/0366.pdf"
venue: "Proceedings of the Twentieth International Joint Conference on Artificial Intelligence"
year: 2007
pages: "2274-2278"
track: ""
keywords:
---

# A Machine Learning Approach for Statistical Software Testing

Nicolas Baskiotis, Michèle Sebag, Marie-Claude Gaudel, Sandrine-Dominique Gouraud

*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.