Zahereel Ishwar Abdul Khalib
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Preferred name
Zahereel Ishwar Abdul Khalib
Official Name
Zahereel Ishwar, Abdul Khalib
Alternative Name
Khalib, Zahereel
Khalib, Z. I.Abdul
Khalib, Z. I.A.
Khalib, Zia • Khalib
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Scopus Author ID
24824279500
Researcher ID
CYK-9763-2022

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  • Publication
    A Review on Recent T-way Combinatorial Testing Strategy
    ( 2017-12-11)
    Nuraminah Ramli
    ;
    Rozmie Razif Othman
    ;
    Zahereel Ishwar Abdul Khalib
    ;
    Muzammil Jusoh
    T-way combinatorial testing aims to generate a smaller test suite size. The purpose of t-way combinatorial testing is to overcome exhaustive testing. Although many existing strategies have been developed for t-way combinatorial testing, study in this area is encouraging as it falls under NP-hard optimization problem. This paper focuses on the analysis of existing algorithms or tools for the past seven years. Taxonomy of combinatorial testing is proposed to ease the analysis. 20 algorithms or tools were analysed based on strategy approach, search technique, supported interaction and year published. 2015 was the most active year in which researchers developed t-way algorithms or tools. OTAT strategy and metaheuristic search technique are the most encouraging research areas for t-way combinatorial testing. There is a slight difference in the type of interaction support. However, uniform strength is the most utilized form of interaction from 2010 to the first quarter of 2017.
      32  2
  • Publication
    Ant colony algorithm to generate t-way test suite with constraints
    ( 2020-06-17)
    Nuraminah Ramli
    ;
    Rozmie Razif Othman
    ;
    Zahereel Ishwar Abdul Khalib
    ;
    Zahereel Ishwar Abdul Khalib
    ;
    Fauzi S.S.M.
    T-way testing is one of the testing techniques offered to generate test suites. It focuses on interactions of input parameters based on strength. Besides that, t-way is used to overcome exhaustive testing problem. Another important aspect in t-way is constraints. It forbids certain interactions of input parameters. Therefore, the final test suite contains only valid interactions. T-way testing is NP-hard problem. No single strategy can always generates the best test suite size at all time for all configurations. Thus, Const-TTSGA strategy has been developed to generate test suites. The strategy supports constraints variable strength. Const-TTSGA is a metaheuristic strategy which applies ant colony algorithm to generate the best test cases. Two types of experiments have been conducted; constraints uniform and variable strength which consists of few other configurations. Results obtained are compared to benchmarked results. Const-TTSGA outperformed other strategy for costraints uniform strength experiments except for one configuration. However, the strategy outperformed other strategies for constraints variable strength experiments.
      3  36