Enhanced approach for test suite optimisation using genetic algorithm. (4th July 2019)

• Record Type: Journal Article
• Authors: Khari, Manju; Kumar, Prabhat; Shrivastava, Gulshan
• Is Part Of: International journal of computer aided engineering and technology. Volume 11:Number 6(2019)
• Page Start: 653
• Page End: 668
• Publication Date: 2019-07-04
• Subjects: bio inspired computation -- genetic -- ant colony optimisation -- ACO -- fitness function; Computer-aided engineering -- Periodicals; 620.00285
• Languages: English
• ISSNs: 1757-2657

Abstract

The software is growing in size and complexity every day due to which strong need is felt by the research community to search for the techniques which can optimise test cases effectively. Search based test cases optimisation has been a key domain of interest for the researchers. Test case optimisation techniques selectively pick up only those test cases from the pool of all available test data which satisfies the predefined testing criteria. The current study is inspired by the ants and genetic behaviour of finding paths for the purpose of finding good optimal solution. The proposed algorithm is GACO algorithm, the genetic algorithm (GA) and ant colony optimisation (ACO) is used to find a suitable solution to solve optimisation problems. The performance of the proposed algorithm is verified on the basis of various parameters namely running time, complexity, efficiency of test cases and branch coverage. The results suggest that proposed algorithm is significantly average percentage better than ACO and GA in reducing the number of test cases in order to accomplish the optimisation target. The inspiring result raises the need to carry out future work.