Pairwise Test Suite Generation Based on Hybrid Artificial Bee Colony Algorithm

Alazzawi, Ammar K.; Rais, Helmi Md; Basri, Shuib; Alsariera, Yazan A.

doi:10.1007/978-981-15-1289-6_13

Cited by 7 publications

(4 citation statements)

References 18 publications

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“…This section shows the experiments of HABCSm strategy. The experiments were conducted to evaluate and compare the efficiency of the proposed HABCSm with our previous work [14][15][16][60][61][62][63][64][65], based on the original Artificial Bee Colony strategy and Hybrid Artificial Bee Colony strategy, as well as with existing published work as adopted from [2,4,17,50,57,66]. Whereas implementation times were neglected due to variances in parameter settings (e.g., SA relies on the Iteration, Cooling schedule, and Starting temperature, while the ABCS relies on the Bee population size, Food source number, Limit and Maximum cycle number) and running platform environment (e.g., the implementation language and data structure).…”

Section: Resultsmentioning

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: Resultsmentioning

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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“…The most popular greedy strategies are jenny [62], In-Parameter-Order-General (IPOG) [63], Automatic Efficient Test Generator (AETG) [64], Classification-Tree Editor eXtended Logics (CTE-XL) [65], Pairwise Independent CT (PICT) [66], Test Vector Generator (TVG) [67], Graph Based Greedy Algorithm (GBGA) [68] and the like. But the main class of CA generation solutions includes methods based on meta-heuristic (such as [71,72,73,74,75,76,77,78,79,80]) and multi stage algorithms [81,82,83], some of them are examined below.…”

Section: B Related Workmentioning

confidence: 99%

Beautiful Mind: a meta-heuristic algorithm for generating minimal covering array

Esfandyari,

Rafe,

Pira

et al. 2024

Preprint

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Today, the application of meta-heuristic algorithms in solving problems is very important. This importance has led to the development of hundreds of types of meta-heuristic algorithms by researchers. The reason for the high number of such algorithms is that an algorithm may be superior to its competitors in a particular problem. Generating a test set in Combinatorial Testing (CT) is one of the thousands of problems that can be solved by meta-heuristic algorithms and hundreds of algorithms have been proposed in this regard. The main challenge in producing a test set in CT is becoming trapped in local optima that several solutions have been offered to overcome this problem. Since the proposed solutions are very slow in terms of time, it is still possible to produce better results by applying other solutions. Continuing our research in the field of CT, we have tried to present a new meta-heuristic solution called Beautiful Mind (BM), which simulates the human way to reach the answer. In fact, the proposed algorithm considers the human intelligence and emotional coefficient to find the answer. The evaluation results show that the proposed approach is much stronger than the existing solutions.

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“…Sheoran et al [41], Sahin et al [42], Boopathi et al [43] and Alazzawi et al [44] have all used ABC in their research. The authors obtained good results.…”

Section: Abcmentioning

confidence: 99%

Automatic Test Data Generation based on the Prime Path Coverage Criterion: A Grouping-based GA Approach

Damia

Esnaashari

Parvizimosaed

2023

Preprint

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Software testing is the process of running an application with the goal of finding bugs and subsequently improving its quality. Software testing, as a key process, plays a role in ensuring the quality of software systems. Testing is currently considered an industry in the field of software. Given that about 40% of the cost of producing any software is spent on testing, creating tools for automatically generating test data will significantly reduce the current costs of software development. This process can be considered an optimization problem, and thus, search algorithms can be used for tackling it. The Genetic Algorithm (GA) is one of the widest algorithms in this field. In this paper, we have proposed a novel GA approach, called Group-based GA (G-GA), which differs from the standard GA algorithm in the following ways. First of all, a new fitness function has been utilized that uses search space information to guide the population. The population is divided into four groups, each of which is updated according to its fitness level. Finally, in the proposed algorithm, the selection operator has been omitted and thus, the algorithm has less complexity and calculations than the standard GA. Also, the proposed algorithm considers a good level of exploration and exploitation at each step. Experiments have shown that the proposed G-GA method, in terms of the convergence speed and the search time, significantly outperforms the basic GA, its variations, PSO, Tabu Search, and Simulated Annealing.

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Pairwise Test Suite Generation Based on Hybrid Artificial Bee Colony Algorithm

Cited by 7 publications

References 18 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

Beautiful Mind: a meta-heuristic algorithm for generating minimal covering array

Automatic Test Data Generation based on the Prime Path Coverage Criterion: A Grouping-based GA Approach

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