Multi-Objective Crow Search and Fruit Fly Optimization for Combinatorial Test Case Prioritization

Abstract

This paper proposes a novel test case prioritization technique, namely Multi- Objective Crow Search and Fruitfly Optimization (MOCSFO) for test case prioritization. The proposed MOCSFO is designed by integrating Crow search algorithm (CSA) and Chaotic Fruitfly optimization algorithm (CFOA). The optimal test cases are selected based on newly modelled fitness function, which consist of two parameters, namely average percentage of combinatorial coverage (APCC) and Normalized average of the percentage of faults detected (NAPFD). The test case to be selected is decided using a searching criterion or fitness based on sequential weighed coverage size. Accordingly, the effective searching criterion is formulated to determine the optimal test cases based on the constraints. The experimentation of the proposed MOCSFO method is performed by considering the performance metrics, like NAPFD, and APCC. The proposed MOCSFO outperformed the existing methods with enhanced NAPFD of 0.7, and APCC of 0.837.

Directed Search Based on Improved Whale Optimization Algorithm for Test Case Prioritization

“…Xing et al [22] used the artificial fish school algorithm (AFSA) to optimize the test case prioritization by using the swarm behavior, foraging behavior, and tail-chasing behavior and verified its effectiveness through experiments. Gouda et al [23] combined the Crow search algorithm and chaotic Drosophila optimization algorithm to enhance the optimization results of test case prioritization. Anu and Sangwan [24] use the bat algorithm to research the TCP problem, and their results are greatly improved compared to traditional methods.…”