Adapting the elitism on greedy algorithm for variable strength combinatorial test cases generation

Homaid, Ameen A. Ba; Alsewari, AbdulRahman A.; Zamli, Kamal Z.; Alsariera, Yazan A.

doi:10.1049/iet-sen.2018.5005

Cited by 6 publications

(3 citation statements)

References 28 publications

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“…In general, existing state-of-the-art strategies for generating the optimal or best test set sizes are divided into two approaches: Algebraic and Computational [17,32]. Mathematicians have typically utilized the Algebraic approach, where a test set is constructed based on the construction of OA.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

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

Self Cite

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“…Implementation of graph theory in the case of finding the shortest distance can be solved using the shortest path search algorithm, which is classified into a single source and multisource [10] where the single source algorithm is the shortest path search algorithm from one source node to various other destination nodes. These algorithms are among the Greedy [11], Dijkstra, and Bellman-Ford algorithms. Meanwhile, multi-source algorithms look for the shortest path by calculating all pairs of vertices in a graph, including the Floyd Warshall, Ant Colony, and Johnson algorithms.…”

Section: Introductionmentioning

confidence: 99%

Comparison Analysis of Graph Theory Algorithms for Shortest Path Problem

Riti,

Iskandar,

Hendra

2023

SISFOKOM

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The Sumba region, Indonesia, is known for its extraordinary natural beauty and unique cultural richness. There are 19 interesting tourist attractions spread throughout the area, but tourists often face difficulties in planning efficient visiting routes. From this case, it can be solved by applying graph theory in terms of searching for the shortest distance which is completed using the shortest path search algorithm. Then these 19 tourist objects are used to build a weighted graph, where the nodes represent the tourist objects and the edges of the graph describe the distance or travel time between these objects. Therefore, this research aims to compare the shortest path search algorithm with parameters to compare the shortest distance results, algorithm complexity and execution time for tourism in the Sumba area. The results of this research involve a comparison of several shortest path search algorithms, with the aim of finding the shortest distance results, algorithm complexity, and execution time for tourism in the Sumba area. Based on the test results of the five algorithms with the parameters that have been prepared, and the findings show that each algorithm has its own characteristics, the results are as follows: Dijkstra's algorithm can be used to calculate the shortest route for single-source and single-destination types. This resembles the Bellman-Ford algorithm, only the Bellman-Ford algorithm can be used simultaneously on graphs that have negative weight values. Meanwhile, the Floyd-Warshall algorithm is suitable for use on the all-pairs type. Then, the Johnson Algorithm can be used to determine the shortest path from all pairs of paths where the destination node is located in the graph. Finally, the Ant Colony algorithm to compute from a node to each pair of destination nodes.

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“…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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Adapting the elitism on greedy algorithm for variable strength combinatorial test cases generation

Cited by 6 publications

References 28 publications

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

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

Comparison Analysis of Graph Theory Algorithms for Shortest Path Problem

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

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