An Enhanced Hybrid Artificial Bee Colony Algorithm for Automated Test Case Generation Using Multi-Objective Optimization 1

Abstract

Automated test case generation plays a vital role in improving software quality by enhancing coverage, reducing human effort, and increasing fault detection efficiency. However, existing approaches face challenges such as: handling multi-objective optimization, maintaining diversity, and avoiding premature convergence. The typical Artificial Bee Colony ABC algorithm is one of many conventional and metaheuristic methods that frequently fail to successfully strike a balance between exploration and exploitation. In order to overcome these constraints, this paper suggests an improved hybrid ABC algorithm for automated test case generation that makes use of a multi-objective optimisation framework. To enhance the quality of the answer, the suggested method combines hybrid search techniques with Pareto-based selection. According to experimental findings, the Hybrid ABC produces a more varied and evenly distributed Pareto front than the traditional ABC in terms of coverage and fault detection potential. The study's findings, hybridisation greatly improves optimisation performance. Future research should concentrate on adaptive hybrid techniques and practical applications.