research-article

Use of genetic algorithm in generation of feasible test data

Authors:

Praveen Ranjan Srivastava,

Priyanka Gupta,

Yogita Arrawatia,

Suman YadavAuthors Info & Claims

ACM SIGSOFT Software Engineering Notes, Volume 34, Issue 2

Pages 1 - 4

https://doi.org/10.1145/1507195.1507217

Published: 28 February 2009 Publication History

Abstract

In recent years researchers have applied the concept of Genetic Algorithm in generation of test data for effective software testing. Several attempts have been made to develop a system to generate test data automatically. The existing such systems do not guarantee to generate test data in only feasible paths. This paper proposes a method to generate feasible test data, using Genetic Algorithm.

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Cited By

Castro MNiño AMuñoz-Caro C(2018)Evaluation and optimal computation of angular momentum matrix elementsWSEAS Transactions on Information Science and Applications10.5555/1852489.18525007:2(263-272)Online publication date: 15-Dec-2018
https://dl.acm.org/doi/10.5555/1852489.1852500
Bhattacharjee GSaluja A(2018)A Path-Oriented Test Data Generation Approach Hybridizing Genetic Algorithm and Artificial Immune SystemComputational Intelligence in Data Mining10.1007/978-981-10-8055-5_58(649-658)Online publication date: 4-Jul-2018
https://doi.org/10.1007/978-981-10-8055-5_58
Gupta PArora ISaha A(2016)A review of applications of search based software engineering techniques in last decade2016 5th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)10.1109/ICRITO.2016.7785022(584-589)Online publication date: Sep-2016
https://doi.org/10.1109/ICRITO.2016.7785022
Show More Cited By

Index Terms

Use of genetic algorithm in generation of feasible test data
1. General and reference
  1. Cross-computing tools and techniques
    1. Metrics
2. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Software defect analysis
        Software testing and debugging
  2. Software notations and tools

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Information

Published In

cover image ACM SIGSOFT Software Engineering Notes

ACM SIGSOFT Software Engineering Notes Volume 34, Issue 2

March 2009

140 pages

ISSN:0163-5948

DOI:10.1145/1507195

Issue’s Table of Contents

Copyright © 2009 Authors.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 February 2009

Published in SIGSOFT Volume 34, Issue 2

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Cited By

Castro MNiño AMuñoz-Caro C(2018)Evaluation and optimal computation of angular momentum matrix elementsWSEAS Transactions on Information Science and Applications10.5555/1852489.18525007:2(263-272)Online publication date: 15-Dec-2018
https://dl.acm.org/doi/10.5555/1852489.1852500
Bhattacharjee GSaluja A(2018)A Path-Oriented Test Data Generation Approach Hybridizing Genetic Algorithm and Artificial Immune SystemComputational Intelligence in Data Mining10.1007/978-981-10-8055-5_58(649-658)Online publication date: 4-Jul-2018
https://doi.org/10.1007/978-981-10-8055-5_58
Gupta PArora ISaha A(2016)A review of applications of search based software engineering techniques in last decade2016 5th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)10.1109/ICRITO.2016.7785022(584-589)Online publication date: Sep-2016
https://doi.org/10.1109/ICRITO.2016.7785022
Huang MZhang CLiang X(2014)Software test cases generation based on improved particle swarm optimizationProceedings of 2nd International Conference on Information Technology and Electronic Commerce10.1109/ICITEC.2014.7105570(52-55)Online publication date: Dec-2014
https://doi.org/10.1109/ICITEC.2014.7105570
Bhattacharyya IBandypopadhyay AGupta BChattopadhyay AChattopadhyay RYasumoto K(2009)Vector GAACM SIGSOFT Software Engineering Notes10.1145/1640162.164016334:6(1-5)Online publication date: 3-Dec-2009
https://dl.acm.org/doi/10.1145/1640162.1640163

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