Application of Artificial Bee Colony Algorithm to Software Testing

Singh, Surender; Chhabra, Jitender Kumar; Kumar, Surendra

doi:10.1109/aswec.2010.30

Cited by 41 publications

(17 citation statements)

References 17 publications

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“…Bee colony optimization is the name given to the colony formed from the mutual understanding and terms work of the natural bees in the process of foraging [6]. The bee colony optimization has been used for understanding the concept of software test suite optimization [8].…”

Section: Bee Colony Optimizationmentioning

confidence: 99%

“…Mala et al has developed a hybrid genetic algorithm based approach for quality improvement and optimization of test cases [19]. Dahiya et al [23] presented an ABC algorithm based approach for automatic generation of structural software tests. Bharati et al [25] have used a combination of BCOGA for a test case generation; however the method of determination of estimated time is not clear in her paper whereas in this paper we have based our approach on fitness score (which is a combination of "Information flow" and "Stack based weight").…”

Section: Related Workmentioning

confidence: 99%

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A Novel Technique for Generation of Test Cases based on Bee Colony Optimization and Modified Genetic Algorithm (BCOmGA)

Dalal¹,

Chhillar²

2013

IJCA

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Software testing is the most important phase of software development life cycle which ensures the quality of software systems. This paper proposes a novel approach for generation of test cases from "Unified Modeling Language" (UML). The Test Case Selection and Reduction is done by using Stratified Sampling, Bee Colony Optimization and Genetic Algorithm. This aims to save cost, time and effort by efficiently minimizing the test suite to ensure maximum coverage. We have applied this technique to a module of a Card Administration System project taken from Software Company. The result shows that the proposed approach effectively detects all the flaws by covering all possible paths of the system. The proposed software testing technique (BCO-mGA) ensures maximum coverage in minimum possible timeframe by executing the final minimized test suite.

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Section: Bee Colony Optimizationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Novel Technique for Generation of Test Cases based on Bee Colony Optimization and Modified Genetic Algorithm (BCOmGA)

Dalal¹,

Chhillar²

2013

IJCA

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“…Dahiya et al [18]applied ABC for structural testing of ten real world programs. Results were not satisfactory in the programs having large input domains and many equality based path constraints.…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach for Regression Testing of Web Applications

Khanna¹,

Chauhan²,

Sharma³

et al. 2018

IJISA

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Abstract-Software testing is one of the most arduous and challenging phase which is to be implemented with the intention of finding faults with the execution of minimum number of test cases to increase the overall quality of the product at the time of delivery or during maintenance phase. With the ever increasing demand of web applications and to meet never ending customer expectations, updations are to incorporate which will be validated through testing process. The structure of the web applications (dynamic website) can be modeled using weighted directed graph which consists of numerous paths starting from homepage (index page) of the website. For thorough testing of the website each and every path of the graph should be tested but due to various constraints like time, money and human resources it becomes very much impractical. This scenario ultimately gives rise to the motivation for the development of technique which reduces the number of paths to be tested so that tester community can test only these numbers of path instead of all possible paths so that satisfactory number of faults can be exposed.In this proposed approach assignment of weights on the edges of the directed graph takes place on the basis of the organization of the website, changes in the structure of the website at page level, experience of the coder and the behaviour of the users who have visited the website earlier. The most fault prone paths are identified using random, greedy, Ant Colony Optimization (ACO) and Artificial Bee Colony Optimization (ABCO) algorithms. Two small size websites and one company's website, and their two versions, were considered for experimentation. Results obtained through ACO and ABCO are promising in nature. This approach will support testing process to be completed in time and delivery of the updated version within given hard deadlines.

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“…The velocity for the new solution is given as follows: v1=w*v+c1*(pbest-x)*rand+c2*(gbest-x)*rand (6) where v1=new solution rand= a random number in the range of [0,1] pbest = personal best solution gbest = global best solution c1,c2 and w are the PSO parameters…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…B et.al and Dahiya. S et.al [4] [6] proposed an approach which aimed to generate the optimal test cases with good path coverage. This paper also explains how the bee agents gather the food source through test cases and identify optimal test cases with respect to fitness functional value.…”

Section: Overview Of Bee Colony Optimizationmentioning

confidence: 99%