Adapting the Elitism on the Greedy Algorithm for Variable Strength Combinatorial Test Cases Generation

Bahomaid, Ameen A. and Alsewari, Abdulrahman A. and Zamli, Kamal Z. and Alsariera, Yazan A. (2018) Adapting the Elitism on the Greedy Algorithm for Variable Strength Combinatorial Test Cases Generation. IET Software. pp. 1-11. ISSN 1751-8814. (In Press / Online First) (In Press / Online First)

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Abstract

A combinatorial testing (CT) is an important technique usually employed in the generation of test cases. The generation of an optimal sized test case is a Non-Deterministic Polynomial hard problem (NP). In recent times, many researchers had developed the various strategies based on the search-based approach to address the combinatorial testing issues. This study presented the most recent variable interaction strength (VS) CT strategy using an enhanced variant in the greedy algorithm. Hence, they are referred to as variable strength modified greedy strategy (VS-MGS). Moreover, the modified strategy supports a VS together with interaction strength up to six. The proposed variant-greedy algorithm employed the elitism mechanism alongside the iteration in order to improve its’ efficiency. This algorithm is invariably called the modified greedy algorithm (MGA). Furthermore, the efficiency and performance of the VS-MGS using MGA were assessed firstly by comparing its results with the original greedy algorithm results and thereafter benchmarked with the results of the existing VS CT strategies. The VS-MGS’s results ultimately revealed that the adaptation of elitism mechanism with iteration in greedy algorithm resulted in an improved efficiency in the process of generating a near-optimal test case set size.

Item Type: Article
Uncontrolled Keywords: combinatorial testing (CT); variable strength modified greedy strategy (VS-MGS)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
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: 24 Jan 2019 01:14
Last Modified: 24 Jan 2019 01:14
URI: http://umpir.ump.edu.my/id/eprint/23683
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