Automatic Generation and Optimization of Test case using Hybrid Cuckoo Search and Bee Colony Algorithm

• Published in: Journal of intelligent systems Vol. 30; no. 1; pp. 59 - 72
• Main Authors: Lakshminarayana, P, SureshKumar, T V
• Format: Journal Article
• Language: English
• Published: Berlin De Gruyter 01.01.2021; Walter de Gruyter GmbH
• Subjects: Algorithms; Cuckoo Search Algorithm; Data search; Heuristic methods; Hybrid Bee Colony Algorithm; Model-driven testing; Optimization; Particle Swarm Optimization; Requirements specifications; Search algorithms; Software; Software development; Software testing; UML diagrams; User requirements
• ISSN: 2191-026X; 0334-1860; 2191-026X
• DOI: 10.1515/jisys-2019-0051

Software testing is a very important technique to design the faultless software and takes approximately 60% of resources for the software development. It is the process of executing a program or application to detect the software bugs. In software development life cycle, the testing phase takes around 60% of cost and time. Test case generation is a method to identify the test data and satisfy the software testing criteria. Test case generation is a vital concept used in software testing, that can be derived from the user requirements specification. An automatic test case technique determines automatically where the test cases or test data generates utilizing search based optimization method. In this paper, Cuckoo Search and Bee Colony Algorithm (CSBCA) method is used for optimization of test cases and generation of path convergence within minimal execution time. The performance of the proposed CSBCA was compared with the performance of existing methods such as Particle Swarm Optimization (PSO), Cuckoo Search (CS), Bee Colony Algorithm (BCA), and Firefly Algorithm (FA).