Efficient Test Case Prioritization Using the Rat Swarm Optimizer Algorithm

Software testing is a critical phase in software development to ensure the reliability and quality of software systems. With the increasing complexity of software applications, the number of test cases can grow significantly, posing challenges in terms of execution time and resource utilization. Test Case Prioritization (TCP) has emerged as a crucial technique to optimize the order of test case execution, aiming to detect defects early in the testing process. This paper presents a novel approach to Test Case Prioritization by harnessing the power of Rat Swarm Optimization (RSO), an algorithm inspired by the collective behavior of rat colonies. Experimental results demonstrate the efficacy of our approach compared to another algorithm. This study evaluates the performance of the proposed method using the execution time taken and compared it with the others algorithm like Ant Colony Optimization (ACO), Artificial Bee Colony (ABC) algorithm, and Firefly algorithm. The results indicate that the Rat Swarm Optimizer (RSO) algorithm is notably more efficient in terms of execution time compared to the other tested algorithms. The results highlight the potential of RSO for applications requiring rapid test case prioritization, where minimizing execution time is critical. This paper underscores the potential of RSO as a viable and innovative solution for Test Case Prioritization, offering software developers and testers a valuable tool for enhancing the efficiency and effectiveness of software testing processes.