An Empirical Study on Bayesian Network-based Approach for Test Case Prioritization

A cost effective approach to regression testing is to prioritize test cases from a previous version of a software system for the current release. We have previously introduced a new approach for test case prioritization using Bayesian Networks (BN) which integrates different types of information to estimate the probability of each test case finding bugs. In this paper, we enhance our BN-based approach in two ways. First, we introduce a feedback mechanism and a new change information gathering strategy. Second, a comprehensive empirical study is performed to evaluate the performance of the approach and to identify the effects of using different parameters included in the technique. The study is performed on five open source Java objects. The obtained results show relative advantage of using feedback mechanism for some objects in terms of early fault detection. They also provide insight into costs and benefits of the various parameters used in the approach.