Metaheuristic Techniques for Test Case Generation

Sahoo, Rashmi Rekha; Ray, Mitrabinda

doi:10.4018/jitr.2018010110

Cited by 13 publications

(3 citation statements)

References 38 publications

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“…There are ten features in the two-valued parameter category (video call, voice messages, video messages, basic colors, high resolution, camera, video player, music player, radio and voice recorder) and eight features in the one-valued parameter category (smart mobile system, calls, messages, GPS, screen, media, voice call, and text messages). From these parameters, the possible configurations that need to be tested are 2 10 × 1 8 = 1024.…”

Section: B Examplementioning

confidence: 99%

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Multiple Black Hole Inspired Meta-Heuristic Searching Optimization for Combinatorial Testing

Al-Sammarraie

Jawawi

2020

IEEE Access

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Combinatorial searching-based software testing (CSST) is a challenging optimization procedure. The achievement of optimal solutions involves a careful formulation of the optimization problem and the selection of an appropriate approach. Meta-heuristic searching procedures have proven to be effective for solving CSST issues. Black hole (BH) optimization is among the more recently developed meta-heuristic searching algorithms. While this approach has been observed to be an effective alternative to particle swarm optimization, its operation is based on only one swarm. To date, no efforts have been made to modify this approach to accommodate multiple swarms. This study proposes a new variant of BH that involves a combination of multiple swarms. The BH optimizer is modified from continuous searching to binary searching and subsequently applied for solving CSST. The evaluation is based on a modified-benchmarking mathematical function and well-known CSST problems. This modified BH method is superior to the original BH and the established particle swarm optimization (PSO) approach. In terms of CSST problems, binary multiple black hole (BMBH) optimizations generate reduction rates between 50% and more than 60% for t = 4 according to the problem. INDEX TERMS Black hole optimization, combinatorial searching-based software testing, meta-heuristic searching, multiple black hole optimization, swarm meta-heuristic.

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Section: B Examplementioning

confidence: 99%

“…In the research of artificial intelligence (AI), opinions vary on the superiority of one searching model over another. While some models generate superior results in certain applications, these methods perform less effectively in others [8]. It is essential that the evaluation of meta-heuristic searching be application-dependent.…”

Section: Introductionmentioning

confidence: 99%

Multiple Black Hole Inspired Meta-Heuristic Searching Optimization for Combinatorial Testing

Al-Sammarraie

Jawawi

2020

IEEE Access

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“…Though, many meta heuristic algorithms like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Cuckoo Search (CS), Ant Colony Optimization (ACO) etc. (Sahoo & Ray, 2018) are applied till now in automatic test case generation, still there is a need for reduction in execution time. Most of them are used to generate test case for one path at a time which is time consuming (Manikumar et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Forest Optimization-Based Test Case Generation for Multiple Paths With Metamorphic Relations

Sahoo

Ray

2022

International Journal of Applied Metaheuristic Computing

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In general multiple paths are covered by multiple runs which is a time consuming task. Now a days, metaheuristic techniques are widely used for path coverage. In order to reduce the time, an efficient method is proposed based on Forest Optimization Algorithm (FOA) with Metamorphic Relations (MRs) that cover multiple paths at a time in one run unlike the traditional search based testing. In the proposed approach, initial test case is generated using FOA, the successive test cases are generated using MRs without undergoing several runs. The motive of using FOA is that the searching mechanism of this algorithm having resemblance with the branch / path coverage techniques of testing. To the best of our knowledge, FOA has not been implemented in software testing. The experimental results are compared with three existing work. The efficiency of simply FOA is also shown how it able to cover multiple paths. The results show that FOA with MRs is more efficient in terms of time consumption and number of paths covered.

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WOA + BRNN: An imbalanced big data classification framework using Whale optimization and deep neural network

et al. 2019

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Metaheuristic Techniques for Test Case Generation

Cited by 13 publications

References 38 publications

Multiple Black Hole Inspired Meta-Heuristic Searching Optimization for Combinatorial Testing

Multiple Black Hole Inspired Meta-Heuristic Searching Optimization for Combinatorial Testing

Forest Optimization-Based Test Case Generation for Multiple Paths With Metamorphic Relations

WOA + BRNN: An imbalanced big data classification framework using Whale optimization and deep neural network

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