Adaptive Random Testing Through Dynamic Partitioning By Localization with Distance and Enlarged Input Domain
Korosh Koochekian Sabor1, Mehran Mohsenzadeh2
1Mr. Korosh koochekian Sabor, Department of Computer Engineering, Science and Research Branch Islamic Azad University, Tehran, Iran.
2Dr. Mehran Mohsenzadeh, Department of Computer Engineering, Science and Research Branch Islamic Azad University, Tehran, Iran.
Manuscript received on 15 November 2012 | Revised Manuscript received on 25 November 2012 | Manuscript Published on 30 November 2012 | PP: 1-5 | Volume-1 Issue-6, November 2012 | Retrieval Number: E0309101612/2012©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Based on the intuition that evenly distributed test cases have more chance for revealing non-point pattern failure regions, various Adaptive Random Testing (ART) methods have been proposed. A large portion of this methods such as ART with random partitioning by localization have edge preference problem. This problem would decrease the performance of these methods. In this article the enlarged input domain approach is used for decreasing edge preference problem in ART with random partitioning by localization. Simulations have shown that failure detection capability of ART by localization with distance and enlarged input domain is comparable and usually better than that of other adaptive random testing approaches.
Keywords: Random Testing, Adaptive Random Testing, Localization, Enlarged Input Domain.
Scope of the Article: Software Domain Modelling and Analysis