Fuzzy Inference System for Test Case Prioritization in Software Testing
This addresses the problem of time-consuming and resource-intensive testing for software developers, but it is incremental as it builds on existing fuzzy logic methods.
The paper tackled the problem of inefficient software testing by proposing a fuzzy logic-based approach for test case prioritization, and the results showed effective ranking and practical applicability in optimizing testing and reducing resource intensity.
In the realm of software development, testing is crucial for ensuring software quality and adherence to requirements. However, it can be time-consuming and resource-intensive, especially when dealing with large and complex software systems. Test case prioritization (TCP) is a vital strategy to enhance testing efficiency by identifying the most critical test cases for early execution. This paper introduces a novel fuzzy logic-based approach to automate TCP, using fuzzy linguistic variables and expert-derived fuzzy rules to establish a link between test case characteristics and their prioritization. Our methodology utilizes two fuzzy variables - failure rate and execution time - alongside two crisp parameters: Prerequisite Test Case and Recently Updated Flag. Our findings demonstrate the proposed system capacity to rank test cases effectively through experimental validation on a real-world software system. The results affirm the practical applicability of our approach in optimizing the TCP and reducing the resource intensity of software testing.