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

Research Output

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A dual band antenna design for future millimeter wave wireless communication at 24.25 GHz and 38 GHz

2017-10-10 , Daud N.N. , Muzammil Jusoh , Hasliza A Rahim @ Samsuddin , Rozmie Razif Othman , Sapabathy T. , Mohd Nizam Osman , Yassin M.N.M. , Kamarudin M.R.

This paper proposes a dual band antenna for future millimeter wave wireless communication. The performance of this dual band antenna is analyzed in term of reflection coefficient when some of the length of the patch antenna was adjustable, overall gain and total efficiency for both frequencies respectively. The size of this presented patch antenna is 4.9 × 7.6 mm2. The dual band antenna was fabricated on a RTRogers5880 with a dielectric constant of £=2.2 and thickness of the substrate is 0.127 mm. The simulated result obtained the reflection coefficient as a requirement of the antenna which is not less than -10 dB for 24.25 GHz and 38 GHz that capable to cover 5G applications. The proposed antenna has achieved a maximum gain up to 5.5 dBi and 4.5 dBi at desired frequencies respectively. All design and simulation are carried out using CST Microwave Studio software. The proposed antenna design could be suitable to be applied as a device to the 5G wireless system.

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Publication

T-way Test Suite Generation Strategy based on Ant Colony Algorithm to Support T-way Variable Strength

2021-03-01 , Nuraminah Ramli , Rozmie Razif Othman , Hendradi R. , Iszaidy Ismail

T-way test suite generation strategy based on Ant Colony algorithm (TTSGA) has been developed to support t-way variable strength testing which tackles exhaustive testing issues. It employs the ant colony optimization algorithm to generate near-optimal number of test suite size. Even though the test suite size is smaller than exhaustive testing, the strategy covers every possible combination of interacting parameters. The strategy has been evaluated by using benchmarked experiments. Results obtained were compared with other existing strategies that support variable strength. It was found that TTSGA produces comparable results with other existing strategies especially for higher strength configurations. Two non-parametric tests, which are Wilcoxon Rank and Friedman test, have been conducted to analyze the results statistically between TTSGA and HSS as only both strategies have complete experiments results. Although the results shows that there is no significant difference of test suite size among them, TTSGA is in the first rank in the Friedman test.

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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.

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Implementation of Sine Cosine Algorithm (SCA) for Combinatorial Testing

2020-03-20 , Altmemi J.M. , Rozmie Razif Othman , Ahmad R. , Ali A.S.

Before being released to the market, software should be screened to ensure that the quality assurance measurement goals have been attained. To achieve this, one of the types of testing sorts is combinatorial testing (CT) aimed at discovering the faults that occur by interacting with the software. A minimization strategy for test cases is indeed important for optimizing test cases and reducing time. As NP hard (where NP is a non-deterministic polynomial) is the problem of generating the minimum test suite of combinatorial interaction testing (CIT). this paper discusses the implementation, and validation of an efficient strategy for t-way testing. The main contribution of the sine cosine algorithm SCA is to show that the strategy was sufficiently competitive as compared to other strategies in terms of the generated test suite size. Unlike most paper. The main contribution of SCA is to show the generation of test data for a high coverage strength (t < 12).

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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.

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A uniform strength t-way test suite generator based on ant colony optimization algorithm to produce minimum test suite size

2021-05-03 , Ramli N. , Rozmie Razif Othman , Hendradi R.

T-way testing can be used to effectively detect faults due to interactions of input parameters, which is difficult to find by other testing techniques. This testing technique able to solve exhaustive testing issue that is impossible to be implemented due to time and cost limitations. Uniform strength t-way testing works by interacting input parameter values uniformly. Pairwise testing (i.e. strength, t = 2) is a well-known types of t-way testing. However, there is a need for strength value to be greater than two. Besides, more faults can be detected by interaction greater than six. Thus, this paper focuses on developing a T-way Test Suite Generator based on Ant Colony algorithm (TTSGA) strategy that supports uniform strength. TTSGA strategy is a metaheuristic based strategy and adopts Ant Colony algorithm. Seven experiments have been performed to see its performance to produce minimum test suite size. Two non-parametric tests, which are Wilcoxon Rank and Friedman test, have been conducted to analyze the results statistically. TTSGA shows competitive results especially for higher strength (i.e. t > 3) and ranked third based on Friedman test.

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SCAVS: Implement Sine Cosine Algorithm for generating Variable t-way test suite

2020-09-21 , Altmemi J.M. , Rozmie Razif Othman , R Badlishah Ahmad

Exhaustive testing occurs between interaction elements (i.e., factor and level). In reality, The software system is often impractical because of other constraints, such as cost and time restrictions. The t -way testing (where t indicates the interaction strength) is one of the powerful software, and practical test techniques apply to detect interaction errors between components (i.e., produce quality minimized test cases). The t-way testing is highly complicated (NP-hard). Therefore, many Meta-heuristic algorithms are used to solve combinatorial problems successfully. In general, one of the most effective optimizations algorithm-based techniques SCA. With respect mentioned above, this paper discusses the implementation Sine Cosine Algorithm for generating a variable strength t-way test suite called SCAVS. The results of the experiments showed that SCAVS outperformed and yielded better test sets than other existing other strategies.

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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.

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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.

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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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