A Kidney Algorithm for Pairwise Test Suite Generation

Homaid, Ameen A. Ba; Alsewari, AbdulRahman A.; Alazzawi, Ammar K.; Z., Zamli Kamal

doi:10.1166/asl.2018.12929

Cited by 13 publications

(7 citation statements)

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“…KA involves two main procedures: filtration (local search) and reabsorption (global search). The Pairwise Kidney Strategy (PKS) was developed based on KA to generate a smaller test suite [49].…”

Section: Related Workmentioning

confidence: 99%

Combinatorial Test Suites Generation Strategy Utilizing the Whale Optimization Algorithm

et al. 2020

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The potentially many software system input combinations make exhaustive testing practically impossible. To address this issue, combinatorial t-way testing (where t indicates the interaction strength, i.e. the number of interacting parameters (input)) was adopted to minimize the number of cases for testing. Complimentary to existing testing techniques (e.g. boundary value, equivalence partitioning, cause and effect graphing), combinatorial testing helps to detect faults caused by the faulty interaction between input parameters. In the last 15 years, applications of meta-heuristics as the backbone of t-way test suite generation have shown promising results (e.g. Particle Swarm Optimization, Cuckoo Search, Flower Pollination Algorithm, and Hyper-Heuristics (HHH), to name a few). Supporting the new free lunch theorem, as well as potentially offering new insights into the whole process of t-way generation, this paper proposes a new strategy with constraint support based on the Whale Optimization Algorithm (WOA). Our work is the first attempt to adopt the WOA as part of a search-based software engineering (SBSE) initiative for t-way test suite generation with constraint support. The experimental results of the test-suite generation indicate that WOA produces competitive outcomes compared to some selected single-based and population-based metaheuristic algorithms.

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Section: Related Workmentioning

confidence: 99%

Combinatorial Test Suites Generation Strategy Utilizing the Whale Optimization Algorithm

et al. 2020

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“…IPOG and GS have the support strength more than 6. Table VI shows the configuration VSCA (N; 3, 3 15 , {C}), where PSTG, GS and ABCVS strategies generate good results for strength up to t = 6, but IPOG and GS produce a better test suite for strength more than 6. However, ACS and SA are not able to generate a result due to not having support strength of more than 3.…”

Section: The Values Inmentioning

confidence: 99%

“…t ≤ 3), it can cover most flaws in a typical software system [7][8][9][10][11][12]. Subsequent research studies although, have proposed the need to support the higher strength interaction, particularly for complex systems [2,4,[13][14][15].…”

Section: Introductionmentioning

confidence: 99%

ABCVS: An Artificial Bee Colony for Generating Variable T-Way Test Sets

Alazzawi¹,

Rais²,

Basri³

2019

IJACSA

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To achieve acceptable quality and performance of any software product, it is crucial to assess various software components in the application. There exist various softwaretesting techniques such as combinatorial testing and covering array. However, problems such as t-way combinatorial explosion is still challenging in any combinatorial testing strategy, as it takes into consideration the entire combinations of input variables. Therefore, to overcome this problem, several optimizations and metaheuristic strategies have been suggested. One of the most effective optimization algorithms based techniques is the Artificial Bee Colony (ABC) algorithm. This paper presents t-way generation strategy for both a uniform and variable strength test suite by applying the ABC strategy (ABCVS) to reduce the size of the test suite and to subsequently enhance the test suite generation interaction. To assess both the effectiveness and performance of the presented ABCVS, several experiments were conducted applying various sets of benchmarks. The results revealed that the proposed ABCVS outweigh the existing based strategies and demonstrated wider interaction between components as opposed to AI-search based and computational based strategies. The results also revealed higher prospect of ABCVS in the aspect of its effectiveness and performance as observed in the majority of case studies. 260 | P a g e www.ijacsa.thesai.org acceptable strategy, known as the Artificial Bee Colony Strategy (ABCVS) continuous to our previous research [40,41] for uniform and variable strength interaction t-way minimal test suite production. It is anticipated that ABCVS will address this problem. In fact, experimental results have revealed that the proposed ABCVS is able to support higher interaction strengths up to t = 6 compared to other Artificial Intelligence (AI)-based strategies. Furthermore, ABCVS it can compete against these other strategies in the majority of the case studies examined in the literature regarding efficiency (test suite generation) and performance (speed) and against other AI-based strategies having higher interaction strengths.The remainder of this paper is structured accordingly. Section II presents the background to this study, (i.e. CA and MCA concepts) which is followed by Section III which surveys "state of the art" testing strategies in this area. Section IV provides an overview of an ABC which is then followed by Section V describing the proposed strategy, consisting of two parts: (1) construction of the covering matrix, and (2) the proposed ABCVS. Section VI illustrates the tuning of the ABCVS parameters. Section VII evaluates the ABCVS by conducting several benchmark experiments in terms of effciency and performance alongside with statistical analysis evaluation by using Wilcoxon signed-rank test in Section VIII. Section IX discusses the advantages, limitations and threats to the validity of the approach, which followed by Section X providing overall conclusions to the study and presenting recommendations for future work. 12

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“…Recently, the efforts of researchers have been focused on metaheuristic algorithms as a foundation for t-way strategies. These include Bat Algorithm [8][9][10][11][12][13], Artificial Bee Colony [14][15][16][17] and Kidney Algorithm [18], to name just a few. However, it is impossible to find meta-heuristic algorithms to compensate for other existing algorithms for all optimization problems.…”

Section: Introductionmentioning

confidence: 99%

HABCSm: A Hamming Based t-way Strategy based on Hybrid Artificial Bee Colony for Variable Strength Test Sets Generation

Alazzawi¹,

Rais²,

Basri³

et al. 2021

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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Search-based software engineering that involves the deployment of meta-heuristics in applicable software processes has been gaining wide attention. Recently, researchers have been advocating the adoption of meta-heuristic algorithms for t-way testing strategies (where t points the interaction strength among parameters). Although helpful, no single meta-heuristic based t-way strategy can claim dominance over its counterparts. For this reason, the hybridization of meta-heuristic algorithms can help to ascertain the search capabilities of each by compensating for the limitations of one algorithm with the strength of others. Consequently, a new meta-heuristic based t-way strategy called Hybrid Artificial Bee Colony (HABCSm) strategy, based on merging the advantages of the Artificial Bee Colony (ABC) algorithm with the advantages of a Particle Swarm Optimization (PSO) algorithm is proposed in this paper. HABCSm is the first t-way strategy to adopt Hybrid Artificial Bee Colony (HABC) algorithm with Hamming distance as its core method for generating a final test set and the first to adopt the Hamming distance as the final selection criterion for enhancing the exploration of new solutions. The experimental results demonstrate that HABCSm provides superior competitive performance over its counterparts. Therefore, this finding contributes to the field of software testing by minimizing the number of test cases required for test execution.

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A Kidney Algorithm for Pairwise Test Suite Generation

Cited by 13 publications

References 0 publications

Combinatorial Test Suites Generation Strategy Utilizing the Whale Optimization Algorithm

Combinatorial Test Suites Generation Strategy Utilizing the Whale Optimization Algorithm

ABCVS: An Artificial Bee Colony for Generating Variable T-Way Test Sets

HABCSm: A Hamming Based t-way Strategy based on Hybrid Artificial Bee Colony for Variable Strength Test Sets Generation

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