CTJ: Input-Output Based Relation Combinatorial Testing Strategy Using Jaya Algorithm

Younis, Mohammed Issam; Alsewari, AbdulRahman A.; Khang, Ng Yeong; Zamli, Kamal Z.

doi:10.21123/bsj.2020.17.3(suppl.).1002

Cited by 8 publications

(5 citation statements)

References 38 publications

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“…However, the combinatorial t-way testing strategies have shown an imperative symbol of tuning parameters setting of an algorithm to obtain a better solution. Combinatorial t-way testing strategies such as Harmony Search Strategy (HSS) [177], Hybrid Artificial Bee Colony (HABC) strategy [317], Sine Cosine Algorithm for generating Variable t-way test suite Strategy (SCAVS) [299], PWiseHA strategy [181], and Combinatorial Testing Strategy using Jaya Algorithm (CTJ) [226], aim to use different number of parameters setting for the adopted algorithm and achieved good optimization.…”

Section: Discussionmentioning

confidence: 99%

“…Jaya Algorithm (JA) is based on the principle of which solution obtained from a problem is then move towards finding the best solution by avoiding worst solution [224]. By considering this concept, JA has been applied in various optimization field like feature selection optimization problem [225], moreover, the JA metaheuristic has recently been introduced around t-way testing [226].…”

Section: Human-based Techniquementioning

confidence: 99%

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Review of Nature Inspired Metaheuristic Algorithm Selection for Combinatorial t-Way Testing

2022

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Metaheuristic algorithm is a very important area of research that continuously improve in solving optimization problems. Nature-inspired is one of the classifications of metaheuristic algorithm that are becoming more popular among researchers for the last decades. Nature-inspired metaheuristic algorithms contributes significantly to tackling many standing complex problems (such as combinatorial tway testing problem) and achieving optimal results. One challenge in this area is combinatorial explosion problem which always intended to find the most optimal final test suite that will cover all combinations of a given interaction strength. As such, test case generation is selected as the most active research area in combinatorial t-way testing as Non-deterministic Polynomial-time hardness (NP-hard). However, not all metaheuristics are effectively adopted in combinatorial t-way testing, some proved to be effective and thus have been popular tools selected for optimization whilst others are not adopted. This research paper outlines hundred and ten (110) outstanding nature-inspired metaheuristic algorithms for the last decades (2001 and 2021) such as Coronavirus Optimization Algorithm, Ebola Optimization Algorithm, Harmony Search, Tiki-Taka Algorithm, and so on. The purpose of this review is to revisit and carry out up-to-date review on these distinguished algorithms with their respective current state of use. This is to inspire future research in the field of combinatorial t-way testing for better optimization. Thus, we found that all metaheuristics has a simple structure to be adopted in different areas for becoming a more efficient in optimization. Finally, we suggested some future paths of investigation for researchers who are interested in the combinatorial t-way testing field to employ more of these algorithms by tuning their parameters setting to achieve an optimal solution.

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Section: Discussionmentioning

confidence: 99%

Section: Human-based Techniquementioning

confidence: 99%

Review of Nature Inspired Metaheuristic Algorithm Selection for Combinatorial t-Way Testing

2022

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“…The decision to choose the best solution requires a cost-benefit analysis of context and solutions at hand. test case generation and optimization are at the forefront 1 . Test case optimization can be further divided into 3 activities TCP, test case selection (TCS), and test case minimization (TCM).…”

Section: Introductionmentioning

confidence: 99%

An exploratory study of history-based test case prioritization techniques on different datasets

Hassan,

Jawawi,

Ahmad

2024

Baghdad Sci.J

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In regression testing, Test case prioritization (TCP) is a technique to arrange all the available test cases. TCP techniques can improve fault detection performance which is measured by the average percentage of fault detection (APFD). History-based TCP is one of the TCP techniques that consider the history of past data to prioritize test cases. The issue of equal priority allocation to test cases is a common problem for most TCP techniques. However, this problem has not been explored in history-based TCP techniques. To solve this problem in regression testing, most of the researchers resort to random sorting of test cases. This study aims to investigate equal priority in history-based TCP techniques. The first objective is to implement different history-based TCP techniques. The second objective is to explore the problem of equal priority in history-based TCP techniques. The third objective is to explore random sorting as a solution to the problem of equal priority in history-based TCP techniques. Datasets of historical records of test cases from conventional and modern sources were collected. History-based TCP techniques were applied to different datasets. The History-based TCP techniques were checked for the problem of equal priority. Then random sorting was used as a solution to the problem of equal priority. Finally, the results were elaborated in terms of APFD and execution time. The results indicate that history-based techniques also suffer from the problem of equal priority like other types of TCP techniques. Secondly, random sorting does not produce optimal results while trying to solve the problem of equal priority in history-based TCP. Furthermore, random sorting deteriorates the results of history-based TCP techniques when employed to solve the problem of equal priority. One should resort to random sorting if no other solution exists. The decision to choose the best solution requires a cost-benefit analysis keeping in view the context and solution under consideration.

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“…Testing all possible combinations of configuration parameters using a sample of all possible configurations is called combinatorial interaction testing (CIT), which is an alternative to exhaustive testing. An exponential increase of test cases is seen during exhaustive testing; however, the number of configuration options grows at a maximum logarithmic rate [1]- [3]. Testing the interactions between multiple configuration options is critical to reducing the likelihood of interacting problems in software that is extremely flexible.…”

Section: Introductionmentioning

confidence: 99%

Innovations in t-way test creation based on a hybrid hill climbing-greedy algorithm

Fadhil

Abdullah

Younis

2023

IJ-AI

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<p>In combinatorial testing development, the fabrication of covering arrays is the key challenge by the multiple aspects that influence it. A wide range of combinatorial problems can be solved using metaheuristic and greedy techniques. Combining the greedy technique utilizing a metaheuristic search technique like hill climbing (HC), can produce feasible results for combinatorial tests. Methods based on metaheuristics are used to deal with tuples that may be left after redundancy using greedy strategies; then the result utilization is assured to be near-optimal using a metaheuristic algorithm. As a result, the use of both greedy and HC algorithms in a single test generation system is a good candidate if constructed correctly. This study presents a hybrid greedy hill climbing algorithm (HGHC) that ensures both effectiveness and near-optimal results for generating a small number of test data. To make certain that the suggested HGHC outperforms the most used techniques in terms of test size. It is compared to others in order to determine its effectiveness. In contrast to recent practices utilized for the production of covering arrays (CAs) and mixed covering arrays (MCAs), this hybrid strategy is superior since allowing it to provide the utmost outcome while reducing the size and limit the loss of unique pairings in the CA/MCA generation.</p>

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CTJ: Input-Output Based Relation Combinatorial Testing Strategy Using Jaya Algorithm

Cited by 8 publications

References 38 publications

Review of Nature Inspired Metaheuristic Algorithm Selection for Combinatorial t-Way Testing

Review of Nature Inspired Metaheuristic Algorithm Selection for Combinatorial t-Way Testing

An exploratory study of history-based test case prioritization techniques on different datasets

Innovations in t-way test creation based on a hybrid hill climbing-greedy algorithm

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