An Approach for Fault Localization Based on Program Slicing and Bayesian

Abstract

The key issue of reducing software cost and improving software reliability is locating defective codes precisely and efficiently. In this paper, we propose a fault localization method which combines program slicing and Bayesian method. First, we perform dynamic program slicing according to the slicing criteria. Then, we calculate the posterior probability according to Bayesian Theory. Finally, we take the posterior probability as the suspicion degree of the statement and rank the statements in the descending order based on suspicion degree. We apply our approach to six open-source programs. The results of the experiments show that the method we propose can improve the precision of fault localization to some extent.