Comparative Analysis for Test Case Prioritization Using Particle Swarm Optimization and Firefly Algorithm

Abstract

Software testing is the most importance phases for software development life cycle. However, it is always time consuming and costly. In order to solve this problem, regression testing is required to be conducted since it can verify the software modifications with zero effect to the software actual features. TCP is one type of the regression testing techniques. It can reduce the cost and time taken. In the area of TCP, there are several algorithms and the most powerful algorithms is the nature inspired algorithms. This study will focus on the comparison analysis of prioritization of test case by using PSO and FA. In order to choose an algorithm with better performance between PSO and FA, they are turned into the form of Python code. Then, PSO and FA are implemented into Case Study A and Case Study B. Their result will be compared and analysis based on the execution time, Big-O, and APFD. The comparison showed that FA is outperform than PSO since FA has the least execution time (0.001 second), less complexity (O(N)) than PSO (O(N3)), and same APFD values (0.520 and 0.600). Thus, FA has better prioritization performance compared to PSO.