An Effective Meta-Heuristic Cuckoo Search Algorithm for Test Suite Optimization

• Published in: Informatica (Ljubljana) Vol. 41; no. 3; pp. 363 - 377
• Main Authors: Khari, Manju, Kumar, Prabhat
• Format: Journal Article
• Language: English
• Published: Ljubljana Slovenian Society Informatika / Slovensko drustvo Informatika 01.09.2017
• Subjects: Algorithms; Automation; Heuristic; Heuristic methods; Initial conditions; International conferences; Mathematical functions; Mutation; Neural networks; Optimization; Search algorithms; Software quality
• ISSN: 0350-5596; 1854-3871

Automation testing is the process of generating test data without any human interventions. In recent times, nature-inspired solutions are planned, tested and validated successfully in many areas for the purpose of optimization. One such meta heuristic technique is Cuckoo Algorithm (CA) that receives its sole inspiration from the behavior of cuckoo, who has the ability to resolve complex issues using simple initial conditions and limited knowledge of the search space. This paper presents a cost effective and time efficient algorithm inspired from cuckoo for optimizing the test data. On comparing the proposed algorithm with existing Firefly Algorithm (FA) and Hill Climbing (HC) algorithms, it was found that CA outperforms both FA and HC in terms of the test data optimization process. The work done in the current study would be helpful to testers in generating optimized test data which would result in saving of both testing cost and time.