Pairwise Test Data Generation Based On Flower Pollination Algorithm

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

doi:10.22452/mjcs.vol30no3.5

Cited by 13 publications

(4 citation statements)

References 25 publications

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“…This approach uses the Levy Flight equation to generate population globally. However, based on the final result, it is found that this approach cannot outperform other approaches in some covering arrays with values 313 and 513822 [4].…”

Section: Related Workmentioning

confidence: 96%

Ontology-based Negative Selection Algorithm For Pairwise Testing

Mohamed Hatta,

Mohamad

2024

Int J Innov Comp

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Pairwise testing is one of the most widely used methods for testing combinations of parameters in software applications. The semantic relationships between parameters may not be taken into consideration by traditional pairwise testing methods, which may result in missing important test cases. It is the objective of this study to evaluate the possibility of improving software testing by combining combinatorial testing algorithms. The proposed technique uses the negative selection algorithm (NSA) approach to change detection and classify the problem domains. To reduce the number of test cases, the researcher combines the model developed using the ontology method for the semantic web with the NSA. In contrast, previous research used a variety of different types of model types when generating test cases. This method proposal proved effective in reducing the testing problem.

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Section: Related Workmentioning

confidence: 96%

Ontology-based Negative Selection Algorithm For Pairwise Testing

Mohamed Hatta,

Mohamad

2024

Int J Innov Comp

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“…The first category uses a single meta-heuristic algorithm as the search engine for the test case. Example of this category includes SA [1], GA [1,2], ACA [2], PSO [3], HS [4], FPA [7], Whale Optimization Algorithm [47] and CS [5]. The second category uses adaptive or hybridization of meta-heuristics algorithms as the search engine.…”

Section: Related Workmentioning

confidence: 99%

“…In the field of software testing, many research adopts meta-heuristic algorithms as the basis of dealing with combinatorial explosion problem (e.g. Simulated Annealing (SA) [1], Genetic Algorithm (GA) [1,2], Ant Colony Algorithm (ACA) [2], Particle Swarm Optimization [3], Harmony Search (HS) [4], Cuckoo Search (CS) [5,6] and Flower Pollination Algorithm (FPA) [7]) related to t-way test suite generation. The t-way test suite generation (where t indicates the interaction strength), involves finding an optimized set of test cases that covers the t-way interaction strength.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Opposition-Based Learning Selection: The Case for Jaya Algorithm

et al. 2021

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Over the years, opposition-based Learning (OBL) technique has been proven to effectively enhance the convergence of meta-heuristic algorithms. The fact that OBL is able to give alternative candidate solutions in one or more opposite directions ensures good exploration and exploitation of the search space. In the last decade, many OBL techniques have been established in the literature including the Standard-OBL, General-OBL, Quasi Reflection-OBL, Centre-OBL and Optimal-OBL. Although proven useful, much existing adoption of OBL into meta-heuristic algorithms has been based on a single technique. If the search space contains many peaks with potentially many local optima, relying on a single OBL technique may not be sufficiently effective. In fact, if the peaks are close together, relying on a single OBL technique may not be able to prevent entrapment in local optima. Addressing this issue, assembling a sequence of OBL techniques into meta-heuristic algorithm can be useful to enhance the overall search performance. Based on a simple penalized and reward mechanism, the best performing OBL is rewarded to continue its execution in the next cycle, whilst poor performing one will miss cease its current turn. This paper presents a new adaptive approach of integrating more than one OBL techniques into Jaya Algorithm, termed OBL-JA. Unlike other adoptions of OBL which use one type of OBL, OBL-JA uses several OBLs and their selections will be based on each individual performance. Experimental results using the combinatorial testing problems as case study demonstrate that OBL-JA shows very competitive results against the existing works in term of the test suite size. The results also show that OBL-JA performs better than standard Jaya Algorithm in most of the tested cases due to its ability to adapt its behaviour based on the current performance feedback of the search process.

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“…RashmiRekhaSahoo et.al proposed a distance based fitness function for generating test inputs for path coverage. [11] Uses search based approach PSO with ICF function Abdullah B. Nasser et.al proposed a Flower Pollination Algorithm [12] which reduced the size of test data thereby saving time and effort. [13] Introduced a modified Bacterial Foraging Technique (BFT) for solving the economic load dispatch problem.…”

Section: Related Workmentioning

confidence: 99%

Bio Inspired Approach for Generating Test data from User Stories

A.Tamizharasi¹

2021

TURCOMAT

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In Agile model where the software prototypes are developed frequently and also rapidly, testing becomes more critical. Generating an effective Test case for complex system is a challenging task involved in software testing. The major research challenge in this area includes the test case generation with limited resources, identifying the essential functional requirement that plays a crucial role and automation of the test case generation process. To solve this issue, a hybridized bio inspired approach is proposed to generate test cases from the user stories which accepts the business requirements as input, processed using NLP and develop functional test cases from it. The proposed algorithm is compared with other existing algorithms and the experimental results proved that the proposed algorithm is more efficient in many cases.

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Pairwise Test Data Generation Based On Flower Pollination Algorithm

Cited by 13 publications

References 25 publications

Ontology-based Negative Selection Algorithm For Pairwise Testing

Ontology-based Negative Selection Algorithm For Pairwise Testing

An Adaptive Opposition-Based Learning Selection: The Case for Jaya Algorithm

Bio Inspired Approach for Generating Test data from User Stories

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