Reducing Redundancy in Software Testing: A K-Means Clustering Approach to Test Case Minimization

Abstract

This study explores a clustering-based approach using k-means clustering to optimize test case minimization, a crucial aspect of enhancing software testing efficiency. Traditional methods often fail to cope with the complexity and scale of modern systems, leading to excessive redundancy and resource consumption. By employing k-means clustering, we effectively group similar test cases into clusters, enabling a reduction in the number of test cases while maintaining adequate test coverage. This approach offers several benefits, including reduced testing time, improved resource utilization, and enhanced fault detection capabilities. Our method ensures that critical areas of software functionality are thoroughly tested, even with fewer test cases, leading to cost savings and efficient resource allocation. The results demonstrate that this clustering-based test case minimization technique is both scalable and practical, making it suitable for large-scale software testing environments