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Abstract
Computer software is in high demand everywhere in the world. The high dependence on software makes software requirements more complicated. As a result, software testing tasks get costlier and challenging due to a large number of test cases, coupled with the vast number of the system requirements. This challenge presents the need for reduction of the system redundant test cases. A combinatorial testing approach gives an intended result from the optimization of the system test cases. Hence, this study implements a combinatorial testing strategy called Artificial Bee Colony Test Generation (ABC-TG) that helps to get rid of some of the current combinatorial testing strategies. Results obtained from the ABC-TG were benchmarked with the results obtained from existing strategies in order to determine the efficiency of the ABC-TG. Finally, ABC-TG shows the efficiency and effectiveness in terms of generating optimum test cases size of some of the case studies and a comparable result with the existing combinatorial testing strategies.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Computational Intelligence; Combinatorial Optimization Problem; Software Testing; Test Data Generation |
| Subjects: | Q Science > QA Mathematics > QA76 Computer software |
| Faculty/Division: | Centre of Excellence: IBM Centre of Excellence Faculty of Computer System And Software Engineering |
| Depositing User: | Dr. AbdulRahman Ahmed Mohammed Al-Sewari |
| Date Deposited: | 17 Nov 2017 02:53 |
| Last Modified: | 25 Sep 2018 08:39 |
| URI: | http://umpir.ump.edu.my/id/eprint/19042 |
| Download Statistic: | View Download Statistics |
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