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
Main Affiliation
Scopus Author ID
36873148100
Researcher ID
GCW-7915-2022

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  • Publication
    Adopting Ant Colony Optimization Algorithm for Pairwise T-Way Test Suite Generation Strategy
    ( 2021-07-26)
    Nuraminah Ramli
    ;
    Rozmie Razif Othman
    ;
    Hendradi R.
    ;
    Fauzi S.S.M.
    ;
    Ismail I.
    ;
    Mohd Zamri Zahir Ahmad
    ;
    Mohd Wafi Nasrudin
    Combinatorial testing or t-way testing (t represents strength) is useful to detect faults due to interactions. Pairwise testing is one type of t-way testing. The technique is effective in reducing the number of test cases without decreasing the level of coverage. Besides, its purpose is to overcome the problem of exhaustive testing that produces a great number of test cases and is impossible to be implemented due to time and cost constraints. Pairwise T-way Test Suite Generation Strategy based on Ant Colony Optimization (pTTSGA) is introduced to generate a near-optimum test suite size. Experiments have been conducted to evaluate the ability of this strategy for pairwise testing. The results are compared to benchmark results. Overall, pTTSGA produces a comparable test suite size.
  • Publication
    Ant colony algorithm to generate t-way test suite with constraints
    ( 2020-06-17)
    Nuraminah Ramli
    ;
    Rozmie Razif Othman
    ;
    Khalib Z.I.A.
    ;
    Mohd Zamri Zahir Ahmad
    ;
    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.
  • Publication
    VS-TACO: A Tuned Version of Ant Colony Optimization for Generating Variable Strength Interaction in T-Way Testing Strategy
    ( 2022-02-24)
    Mohd Zamri Zahir Ahmad
    ;
    Rozmie Razif Othman
    ;
    Nuraminah Ramli
    ;
    Mohd Shaiful Aziz Rashid Ali
    Ever since, software technologies have been through a rapid evolution. In a real application, the interaction between input variables may vary, thus the exhaustive testing is no longer practical since it is time-consuming and lead to combinatorial explosion. One of the strategies that able to cater fault due to the interaction is Ant Colony Optimization (ACO) algorithm. Typically, amount of ants in the ACO algorithm is fixed at certain number while the search space technique (i.e. to explore or exploit new possible solutions) is randomized for each iteration in the entire algorithm, are potentially affect the optimization's efficiency. Thus this paper proposes a new variant of ACO algorithm called as a tuned version of ACO for generating variable strength interaction in t-way testing strategy (VS-TACO). VS-TACO applied a Mamdani fuzzy logic in order to dynamically choose the number of ant and decide which search space technique to be used. Experiments that have been conducted on VS-TACO and benchmarked with other strategies, shows VS-TACO produce a competitive result in term of test suite size.
  • Publication
    A Modified Artificial Bee Colony Based Test Suite Generation Strategy for Uniform T-Way Testing
    ( 2020-03-20)
    Rashid Ali M.S.A.
    ;
    Rozmie Razif Othman
    ;
    Zainor Ridzuan Yahya
    ;
    Mohd Zamri Zahir Ahmad
    Today, t-way testing has been widely known with the ability to reduce test suite size compared to exhaustive testing. At the same time, it has been proven by many researchers to provide maximum bug detection capability. Thus, various t-way strategies were developed since the past three decades. The paper proposed a new test generation strategy, named Modified Artificial Bee Colony T-Way Test Suite Generation (MABCTS). It supports uniform strength t-way testing. Experimentation results are compared with present strategies and produced comparable results. Since t-way testing is considered an NP-hard problem, there are no strategies that can be demanded to produce the best results.
  • 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.
  • 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.
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