Efficient Test Case Prioritization Using Bayesian Networks: A Probabilistic Approach for Improved Software Testing
Neelam Rawat, Vikas Somani, Arun Kumar Tripathi · 2024
Efficient test case prioritization is a crucial aspect of software testing, aiming to identify and execute test cases that are more likely to uncover defects earlier in the testing process. In this research paper, we propose a novel approach to test case prioritization utilizing Bayesian networks. Bayesian networks offer a probabilistic modelling framework that can capture dependencies and relationships among various factors affecting the likelihood of a test case revealing defects. Our methodology involves the construction of a Bayesian network model that incorporates information about the software under test, historical defect data, and other relevant factors. By leveraging probabilistic inference within the Bayesian network, we assign priority scores to individual test cases, allowing us to rank them in order of importance for testing. This prioritization strategy enables testing teams to allocate their resources effectively, focusing on the most critical test cases and potentially reducing testing time and costs. We present experimental results demonstrating the effectiveness of our Bayesian network-based test case prioritization approach on real-world software projects. Our findings indicate that this method not only improves defect detection capabilities but also enhances overall testing efficiency. We discuss the practical implications and potential applications of Bayesian network-based prioritization in software testing and highlight areas for future research and refinement. This research contributes to the field of software testing by offering a data-driven and probabilistic approach to test case prioritization, which can lead to more reliable software products and streamlined testing processes. Moreover, it underscores the value of Bayesian networks as a powerful tool for decision-making in software engineering practices.