Leveraging Clustering Algorithms for Optimizing Test Case Prioritization in Software Development

Abstract

In the field of software development, ensuring the quality and reliability of software through rigorous testing is crucial. However, as software systems grow more complex, managing the vast number of test cases becomes increasingly challenging. To address this issue, test case prioritization techniques are essential, as they help identify and execute the most critical test cases first. This research paper proposes a novel approach to test case prioritization by leveraging clustering techniques, specifically K-means, in conjunction with machine learning algorithms. We investigate the advantages of K-means clustering for grouping similar test cases and enhancing prioritization efficiency. Additionally, we explore various machine learning algorithms, including Decision Trees (DT), Random Forests (RF), and Neural Networks (NN), and compare their effectiveness against traditional prioritization methods such as code coverage-based and risk-based techniques. Our study evaluates these methods using multiple datasets and metrics, including the number of test cases executed, fault detection rate, and execution time. The experimental results reveal that integrating K-means clustering with machine learning algorithms can significantly improve test case prioritization by reducing the number of test cases executed while maintaining or even enhancing fault detection rates. We also discuss the limitations of our approach and suggest directions for future research in optimizing test case prioritization with advanced machine learning techniques. Our findings offer valuable insights into developing more efficient software testing practices, ultimately contributing to the enhanced quality and reliability of software systems.