An Optimal Software Test Case Mechanism using Grey Wolf-FireFly Method

Abstract

Software testing is a major technique for designing the software without fault and use the resources efficiently for the development of any software. The software bugs can be detected by the process of executing an application or a program. Test Case Generation (TCG) is a method to identify the test data and satisfy the software testing criteria. An automatic TC technique automatically determines where the TC or test data generates by utilizing search based optimization method. In this paper, Gref-Wolf and Firefly algorithm (GWFF) method is used for optimization of TC and generation of path coverage within the minimal execution time. In general, the challenging task is constraint handling in which the fitness function is directly affected by updating the positions of the searching agents. In proposed method, there is no direct relation between the fitness function and search agents, because the agents update their positions with respect to the locations of alpha, beta and delta. Experimental results showed that the GWFF method helped to generate path convergence within minimal execution time. Comparing to the existing methods such as Bee Colony Algorithm (BCA), Particle Swarm Optimization (PSO) and Cuckoo Search (CS), the proposed GWFF method provided better performance in terms of fitness values.

A Systematic Literature Review on Robust Swarm Intelligence Algorithms in Search-Based Software Engineering

“…Tey utilized WOA for the weight optimization process. Rastogi [30] used the GWO and FA (GWFF) methods to optimize the Test Case (TC) and generate path coverage in less execution time. Te GWFF showed better performance in terms of ftness values when compared with Particle Swarm Optimization (PSO), Bee Colony Algorithm (BCA), and Cuckoo Search (CS).…”