Learning Cuckoo Search Strategy for t-way Test Generation

Nasser, Abdullah B.; Alsewari, AbdulRahman A.; Zamli, Kamal Z.

doi:10.1007/978-981-13-0755-3_8

Cited by 10 publications

(13 citation statements)

References 38 publications

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“…HS is a potent technique to produce the minimum array size for the test suite, although it is www.ijacsa.thesai.org slow. Some of the most important heuristic search algorithms include SA [9-11, 20, 21], GA [11,12,[22][23][24][25][26][27]62], ACA [8,12], PSO [7,[28][29][30], Cuckoo Search [3,33,34], Tabu Search (TS) [11,63] and Harmony Search (HS) [4,31,32]. Initially, most algorithms were implemented in order to produce pairwise interaction (2-way), as by Stardom [8] such as; SA, GA and TS.…”

Section: Heuristic Search Algorithmmentioning

confidence: 99%

“…By drawing inspiration from the SBSE technique, various strategies have already been suggested towards the problem of uniform and variable strength interaction (e.g. Simulated Annealing (SA) [9-11, 20, 21], Genetic Algorithm (GA) [12,[21][22][23][24][25][26][27], Practice Swarm Optimization (PSO) [7,[28][29][30], Ant Colony Algorithm (ACA) [8,12], Harmony Search Algorithm (HS) [4,31,32], Cuckoo Search (CS) [3,33,34], and the Bat Algorithm (BA) [35][36][37][38][39]). Even though relevant strategies have been found to be useful based on AI, they are noted in the literature as being slow due to costly computations [3,4,7].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Heuristic Search Algorithmmentioning

confidence: 99%

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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“…For the parameters setup, we adopted the tuned FPA parameters as discussed in Section 5.1. For the other component parameters, such as mutation rate and elitism probability, we took the recommended values (i.e., mutation rate = 0.03 and elitism probability = 0.25) as published in [ 61 ]. For a clear perspective, Table 4 depicts the parameters that are adopted for the meta-heuristic strategies [ 16 , 17 , 49 ] in our experiments.…”

Section: Experiments and Evaluationmentioning

confidence: 99%

Hybrid flower pollination algorithm strategies for t-way test suite generation

et al. 2018

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The application of meta-heuristic algorithms for t-way testing has recently become prevalent. Consequently, many useful meta-heuristic algorithms have been developed on the basis of the implementation of t-way strategies (where t indicates the interaction strength). Mixed results have been reported in the literature to highlight the fact that no single strategy appears to be superior compared with other configurations. The hybridization of two or more algorithms can enhance the overall search capabilities, that is, by compensating the limitation of one algorithm with the strength of others. Thus, hybrid variants of the flower pollination algorithm (FPA) are proposed in the current work. Four hybrid variants of FPA are considered by combining FPA with other algorithmic components. The experimental results demonstrate that FPA hybrids overcome the problems of slow convergence in the original FPA and offers statistically superior performance compared with existing t-way strategies in terms of test suite size.

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“…To perform the experiments, we adopt the Intel (R) Core (TM) i7-3770 CPU@ 3.40 GHz -3.40 GHz, 4GB of RAM, Windows 7 Professional, and 32-bit Operating System. For parameters adjustment, we have adopted Pa = 0.8, maximum iteration = 700, pollen size = 50 and switch probability = 0.25, based on the experiments recommended values by (Ahmed et al, 2015;Nasser et al, 2015;Yang, 2012). Each cell displays the minimum test suite size for each the existing strategies where the best (marked as shaded cell) and the average test suite size is recorded.…”

Section: Evaluation Of Efpamentioning

confidence: 99%

An elitist-flower pollination-based strategy for constructing sequence and sequence-less t-way test suite

Nasser

Zamli

Alsewari

et al. 2018

IJBIC

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HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Learning Cuckoo Search Strategy for t-way Test Generation

Cited by 10 publications

References 38 publications

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

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

Hybrid flower pollination algorithm strategies for t-way test suite generation

An elitist-flower pollination-based strategy for constructing sequence and sequence-less t-way test suite

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