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

Software testing is the most importance phase 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. Test Case Prioritization (TCP) is one type of regression testing techniques. It can reduce the cost and time taken. In the area of TCP, there are several algorithms. This study will focus on the comparison analysis of prioritization of test case by using Particle Swarm Optimization (PSO) and Firefly Algorithm (FA). In order to choose an algorithm with better performance between PSO and FA, they are converted into Python code. Then, PSO and FA are implemented into Case Study A and Case Study B. Their result will be compared and analyzed based on the execution time, Big-O, and APFD. The comparison showed that FA outperforms than PSO since FA has the least execution time (0.001 second), less complexity (O(N)) than PSO (O(N³)), and similar APFD values (0.520 and 0.600). Thus, FA has better prioritization performance compared to PSO. [ABSTRACT FROM AUTHOR]

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