Mohd Shaiful Aziz Rashid Ali
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Preferred name
Mohd Shaiful Aziz Rashid Ali
Official Name
Mohd Shaiful Aziz , Rashid Ali
Alternative Name
Aziz Rashid, Mohd Shaiful
Shaiful, M.
Ali, Mohd Shaiful Aziz Rashid
Rashid Ali, Mohd Shaiful Aziz
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Scopus Author ID
57192403982
Researcher ID
V-9522-2019

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  • 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 Tuned Version of Ant Colony Optimization Algorithm (TACO) for Uniform Strength T-way Test Suite Generator: An Execution's Time Comparison
    ( 2021-07-26)
    Ahmad M.Z.Z.
    ;
    Rozmie Razif Othman
    ;
    Mohd Shaiful Aziz Rashid Ali
    ;
    Nuraminah Ramli
    ;
    Nasrudin M.W.
    ;
    Halim A.A.A.
    Software testing is one of important phase in software development. The capabilities of t-way testing to cater bugs due to interactions while reducing the test suite size compare to exhaustive testing has been proven in past decades. However, the execution's time of the t-way strategy also should be given attention as it could increase the productivity of the testing phase. Thus, this paper proposed a tune version of ant colony optimization algorithm (TACO). TACO is metaheuristic strategy where it adopts ant colony optimization in generating test suites. As further improvement, TACO also integrated with fuzzy logic to dynamically select amount of ant in the algorithm. TACO able to supports uniform strength t-way testing. Experiment result shows that TACO produce a remarkable result of test suite size and execution's time compared to other strategy for uniform strength t-way testing.