Rozmie Razif Othman
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Preferred name
Rozmie Razif Othman
Official Name
Rozmie Razif, Othman
Alternative Name
Razif Othman, Rozmie
Othman, Rozmie R.
Othman, Rozmie Razif Bin
Othman, R. R.
Razif bin Othman, Rozmie
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Scopus Author ID
36873148100
Researcher ID
GCW-7915-2022

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  • 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.
      1
  • Publication
    A Self-Adapting Ant Colony Optimization Algorithm Using Fuzzy Logic (ACOF) for Combinatorial Test Suite Generation
    ( 2020-03-20)
    Mohd Zamri Zahir Ahmad
    ;
    Rozmie Razif Othman
    ;
    Mohd Shaiful Aziz Rashid Ali
    ;
    Nuraminah Ramli
    Software testing is one of most crucial phase in software development life cycle (SDLC). The main function of testing is to cater bugs between interactions of the inputs. It is not possible to eliminate all bugs in one system but by using a suitable testing optimization, it can provide a good enough solution for it. Reducing effort on this phase is not only could lead to numerous bugs between the input interactions, but it also leads to a greater loss such as loss of profits, reputations and even loss of life. Generally, there are three categories of combinatorial testing techniques which is computational, metaheuristic, and hyper heuristic. Ant colony optimization (ACO) is a heuristic technique where its mimic the nature of ants in finding the best route from the nest to the food node and vice versa. Many optimization problems have been solved by using ACO. This paper is to proposed a self-adapting ant colony optimization algorithm using fuzzy logic (ACOF) for combinatorial test suite generation, where it will dynamically determine number of ants and edge selection (i.e. either to explore or to exploit) based on percentage of remaining tuple list and covered test cases.
      1