Nuraminah Ramli
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Preferred name
Nuraminah Ramli
Official Name
Nuraminah, Ramli
Alternative Name
Ramli, Nuraminah Bt
Ramli, N.
Nuraminah, R.
Ramli, N. H.
Main Affiliation
Scopus Author ID
57188752016
Researcher ID
DMH-8769-2022

Research Output

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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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Real-time in-vehicle air quality monitoring system using machine learning prediction algorithm

2021-08-01 , Goh C.C. , Latifah Munirah Kamarudin , Ammar Zakaria , Nishizaki H. , Nuraminah Ramli , Mao X. , Syed Muhammad Mamduh Syed Zakaria , Kanagaraj E. , Abdul Syafiq Abdull Sukor , Elham M.F.

This paper presents the development of a real-time cloud-based in-vehicle air quality monitoring system that enables the prediction of the current and future cabin air quality. The designed system provides predictive analytics using machine learning algorithms that can measure the drivers’ drowsiness and fatigue based on the air quality presented in the cabin car. It consists of five sensors that measure the level of CO2, particulate matter, vehicle speed, temperature, and humidity. Data from these sensors were collected in real-time from the vehicle cabin and stored in the cloud database. A predictive model using multilayer perceptron, support vector regression, and linear regression was developed to analyze the data and predict the future condition of in-vehicle air quality. The performance of these models was evaluated using the Root Mean Square Error, Mean Squared Error, Mean Absolute Error, and coefficient of determination (R2 ). The results showed that the support vector regression achieved excellent performance with the highest linearity between the predicted and actual data with an R2 of 0.9981.

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Real-Time In-Vehicle air quality monitoring system using machine learning prediction algorithm

2021 , Chew Cheik Goh , Latifah Munirah Kamarudin , Ammar Zakaria , Hiromitsu Nishizaki , Nuraminah Ramli , Xiaoyang Mao , Syed Muhammad Mamduh Syed Zakaria , Ericson Kanagaraj , Abdul Syafiq Abdull Sukor , Md. Fauzan Elham

This paper presents the development of a real-time cloud-based in-vehicle air quality monitoring system that enables the prediction of the current and future cabin air quality. The designed system provides predictive analytics using machine learning algorithms that can measure the drivers’ drowsiness and fatigue based on the air quality presented in the cabin car. It consists of five sensors that measure the level of CO2, particulate matter, vehicle speed, temperature, and humidity. Data from these sensors were collected in real-time from the vehicle cabin and stored in the cloud database. A predictive model using multilayer perceptron, support vector regression, and linear regression was developed to analyze the data and predict the future condition of in-vehicle air quality. The performance of these models was evaluated using the Root Mean Square Error, Mean Squared Error, Mean Absolute Error, and coefficient of determination (R2). The results showed that the support vector regression achieved excellent performance with the highest linearity between the predicted and actual data with an R2 of 0.9981.

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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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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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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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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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