Authors: Esmaeel Nikravan; Saeed Parsa
Addresses: Faculty of Computer Engineering, Iran University of Science and Technology, Tehran, Iran ' Faculty of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
Abstract: Adaptive random testing (ART) subsumes a family of random testing techniques with an effective improvement. It is based on the observation that failure causing inputs tend to be clustered together. Hence the ART methods spread test cases more evenly within the input domain to improve the fault-detection capability of random testing. There have been several implementations of ART based on different intuitions and principles with their own advantages and disadvantages. In the different variants of ART methods, the majority of them use a variety of distance calculations, with corresponding computational overhead. The newly methods try to decrease computational overhead while maintaining the performance through partitioning the input domain. We outline a new partitioning-based ART algorithm with a hybrid search method and demonstrate experimentally that it can further improve the performance, with considerably lower overhead than other ART algorithms.
Keywords: software testing; random testing; adaptive random testing; ART; test data generation.
International Journal of Computing Science and Mathematics, 2020 Vol.11 No.3, pp.209 - 221
Received: 28 Jul 2017
Accepted: 06 Oct 2017
Published online: 20 Apr 2020 *