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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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 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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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.

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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.

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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.

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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.

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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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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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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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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.

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