Fuzzy Inference System for Test Case Prioritization in Software Testing

TL;DR

This paper presents a fuzzy logic-based method for test case prioritization in software testing, aiming to improve testing efficiency by ranking test cases based on failure rate, execution time, and other factors.

Contribution

It introduces a novel fuzzy inference system that automates test case prioritization using fuzzy variables and rules, enhancing testing effectiveness.

Findings

Effective ranking of test cases demonstrated through real-world validation

Reduces resource consumption in software testing processes

Practical applicability confirmed for large, complex systems

Abstract

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.