A hybrid-adaptive neuro-fuzzy inference system for multi-objective regression test suites optimization

A hybrid-adaptive neuro-fuzzy inference system for multi-objective regression test suites... Regression testing is a mandatory activity of software development life cycle, which is performed to ensure that modi- fications have not caused any adverse effects on the system’s functionality. With every change in software in the main- tenance phase, the size of regression test suite grows as new test cases are written to validate changes. The bigger size of regression test suite makes the testing expensive and time-consuming. Optimization of regression test suite is a possible solution to cope with this problem. Various techniques of optimization have been proposed; however, there is no perfect solution for the problem and therefore, requires better solutions to improve the optimization process. This paper presents a novel technique named as hybrid-adaptive neuro-fuzzy inference system tuned with genetic algorithm and particle swarm optimization algorithm that is used to optimize the regression test suites. Evaluation of the proposed approach is performed on benchmark test suites including ‘‘previous date problem’’ and ‘‘Siemens print token.’’ Experimental results are com- pared with existing state-of-the-art techniques, and results show that the proposed approach is more effective for the reduction in a regression test suites with higher requirement coverage. The size of regression test suites can be reduced up to http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Neural Computing and Applications Springer Journals

A hybrid-adaptive neuro-fuzzy inference system for multi-objective regression test suites optimization

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Publisher
Springer London
Copyright
Copyright © 2018 by The Natural Computing Applications Forum
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Data Mining and Knowledge Discovery; Probability and Statistics in Computer Science; Computational Science and Engineering; Image Processing and Computer Vision; Computational Biology/Bioinformatics
ISSN
0941-0643
eISSN
1433-3058
D.O.I.
10.1007/s00521-018-3560-8
Publisher site
See Article on Publisher Site

Abstract

Regression testing is a mandatory activity of software development life cycle, which is performed to ensure that modi- fications have not caused any adverse effects on the system’s functionality. With every change in software in the main- tenance phase, the size of regression test suite grows as new test cases are written to validate changes. The bigger size of regression test suite makes the testing expensive and time-consuming. Optimization of regression test suite is a possible solution to cope with this problem. Various techniques of optimization have been proposed; however, there is no perfect solution for the problem and therefore, requires better solutions to improve the optimization process. This paper presents a novel technique named as hybrid-adaptive neuro-fuzzy inference system tuned with genetic algorithm and particle swarm optimization algorithm that is used to optimize the regression test suites. Evaluation of the proposed approach is performed on benchmark test suites including ‘‘previous date problem’’ and ‘‘Siemens print token.’’ Experimental results are com- pared with existing state-of-the-art techniques, and results show that the proposed approach is more effective for the reduction in a regression test suites with higher requirement coverage. The size of regression test suites can be reduced up to

Journal

Neural Computing and ApplicationsSpringer Journals

Published: Jun 6, 2018

References

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