Artificial Bee Colony Algorithm for t-Way Test Suite Generation

Alazzawi, Ammar K.; Rais, Helmi Md; Basri, Shuib

doi:10.1109/iccoins.2018.8510601

Cited by 15 publications

(8 citation statements)

References 14 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%

“…Recently, researchers began focusing on meta-heuristic algorithms as a main algorithm for tway test set generation strategies such as Bat Algorithm (BA) [8][9][10][11][12][13], Hill Climbing (HC) [28], Simulated Annealing (SA) [27,28,31,41], Tabu Search (TS) [47,48], Artificial Bee Colony (ABC) [14][15][16], Particle Swarm Optimization (PSO) [49], Cuckoo Search (CS) [50], Ant Colony Algorithm (ACA) [51], Genetic Algorithm (GA) [52], Kidney Algorithm (KA) [18] and others. The most essential algorithm used for a 2-way test set generation ( also termed pairwise test set generation) is HC [28].…”

Section: Related Workmentioning

confidence: 99%

“…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%

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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%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…Several variants of ABC have also been implemented in t-way testing. For instance, the Pairwise Artificial Bee Colony algorithm (PABC) [45] was implemented in 2-way testing and the Artificial Bee Colony Strategy (ABCS) [46] was applied for a higher interaction strength of up to ten (i.e. t ≤ 10).…”

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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“…The choice of t-way interaction strength depended on the test requirements (Alazzawi, Rais, & Basri, 2019). Typically, based on the empirical results within the literature, t might be selected from t = 2 until t = 6 depending on the parameters of choice.…”

Section: Flower Pollination Algorithm With Metropolis-hastings Criteriamentioning

confidence: 99%

Dynamic Probability Selection for Flower Pollination Algorithm Based on Metropolishastings Criteria

Zamli

Din

Nasser

et al. 2020

Journal of Information and Communication Technology

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Flower Pollination Algorithm (FPA) is a relatively new meta-heuristic algorithm that adopts its metaphor from the proliferation role of flowers in plants. Having only one parameter control (i.e. the switch probability, pa) to choose from the global search (i.e. exploration) and local search (i.e. exploitation) is the main strength of FPA as compared to other meta-heuristic algorithms. However, FPA still suffers from variability of its performance as there is no one size that fits all values for pa, depending on the characteristics of the optimisation function. This paper proposed flower pollination algorithm metropolis-hastings (FPA-MH) based on the adoption of Metropolis-Hastings criteria adopted from the Simulated Annealing (SA) algorithm to enable dynamic selection of the pa probability. Adopting the problem of t-way test suite generation as the case study and with the comparative evaluation with the original FPA, FPA-MH gave promising results owing to its dynamic and adaptive selection of search operators based on the need of the current search.

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Artificial Bee Colony Algorithm for t-Way Test Suite Generation

Cited by 15 publications

References 14 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

Combinatorial Test Suites Generation Strategy Utilizing the Whale Optimization Algorithm

Dynamic Probability Selection for Flower Pollination Algorithm Based on Metropolishastings Criteria

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