Optimization of object-oriented testing using firefly algorithm

Saha, Anju

doi:10.1080/02522667.2017.1372135

Cited by 18 publications

(5 citation statements)

References 12 publications

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“…A survey of the GA approach to address different issues that were encountered in software testing was presented by Sharma et al (2013). Sharma and Saha et al (2017) had proposed a new firefly algorithm, which was meta-heuristic in the state chart experimenting of a total of five standards in an object-oriented application along with an optimized test case that is generated. Once this is done, the results will be compared with the currently existing algorithm known as the Ant Colony Optimization (ACO), which is nature-inspired.…”

Section: Literature Surveymentioning

confidence: 99%

RETRACTED ARTICLE: A multi objective binary bat approach for testcase selection in object oriented testing

Geetha

Mala

2020

J Ambient Intell Human Comput

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Time and resources are usually neglected areas in the life cycle of software development. So, these become the primary constraints in software testing. Optimization of a test suite is quite crucial in reducing the complexity of the testing phase and selection of the test cases by eliminating redundant data; this is critical for defining the strategies. Most of the work in literature employs single-objective optimization methods. Though these are not always efficient, these play a critical role in the selection of a test case. Test case selection is, however, non-deterministic. Selection of test cases using Parallel Programming is treated as a complex task due to the need for higher performance in Parallel Computing. Parallel Computing can be stated as a combination of Computational mechanisms and Mathematical techniques. Hence, this investigation proposes a novel BAT algorithm for multi-objective optimization. It has code coverage as well as Object-oriented testing strategies. Comparing the experimental results with the Genetic Algorithm (GA), it is observed that the proposed method has faster convergence with adequate code coverage.

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Section: Literature Surveymentioning

confidence: 99%

RETRACTED ARTICLE: A multi objective binary bat approach for testcase selection in object oriented testing

Geetha

Mala

2020

J Ambient Intell Human Comput

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“…After doing empirical study it is found that firefly, generates less redundant test cases as compared to ACO. Sharma & Saha [43] applied Firefly algorithm in model based testing. In this suggested approach a program is represented as a state transition diagram, based on its behavior on inputs.…”

Section: B Fa In Functional Testingmentioning

confidence: 99%

Research on use of Nature Inspired Algorithms in Software Testing

Sharma¹,

Rizvi²,

Sharma³

2019

IJITEE

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The intention behind software testing is discovering defects in the developed software, making it error-free, robust and trustworthy. A large quantum of total efforts in the development of software is done on software testing. These efforts may include time, cost and most importantly manpower. The number of efforts required in software testing may be reduced if test data is generated automatically, without trade off the quality of the developed software. In literature, various nature inspired algorithms are used for the optimization of the software testing process. This article is a brief study on the applications of nature inspired algorithms in software testing. To keep the study succinct this study considers only two widely used testing types, structural and functional testing and literature available, since 2010 by considering only SCI or SCOPUS indexed publications.

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“…Genetic Algorithm have been used in [14] for test case generation for path testing, in [15] for objected oriented program using UML modeling, in [16] [24]. Firefly optimization algorithm have been used in [24][25] [26]. A CSA based frame work is also proposed in [27].…”

Section: Related Workmentioning

confidence: 99%

Test Case Generation for Data Flow Testing using Cuckoo Search Algorithm

2019

IJRTE

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Software testing consumes the major portion of the total efforts required for software development. This activity is very time consuming and labor intensive. It is very hard to do testing in optimal manner. In this paper a new approach is proposed, which uses the nature inspired stochastic algorithm called Cuckoo Search Algorithm (CSA) for the automatic generation of test data for data flow testing. This approach considers all def-use as test adequacy criteria. For assistance to CSA in the state space a new fitness function is also proposed by using the concept of dominator tree and branch distance in a CFG. To validate the proposed approach experiments are carried out on 10 benchmarked programs and findings are contrasted with earlier work done in this domain. Further in order to prove that proposed approach performs better than the above mentioned approaches a statistical difference test (T-test) is also performed.

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Optimization of object-oriented testing using firefly algorithm

Cited by 18 publications

References 12 publications

RETRACTED ARTICLE: A multi objective binary bat approach for testcase selection in object oriented testing

RETRACTED ARTICLE: A multi objective binary bat approach for testcase selection in object oriented testing

Research on use of Nature Inspired Algorithms in Software Testing

Test Case Generation for Data Flow Testing using Cuckoo Search Algorithm

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