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Test case selection using multi-objective Evolutionary Algorithms

S. Raheja1 , R. Singh2

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-7 , Page no. 1478-1484, Jul-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i7.14781484

Online published on Jul 31, 2018

Copyright © S. Raheja, R. Singh . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: S. Raheja, R. Singh, “Test case selection using multi-objective Evolutionary Algorithms,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.7, pp.1478-1484, 2018.

MLA Style Citation: S. Raheja, R. Singh "Test case selection using multi-objective Evolutionary Algorithms." International Journal of Computer Sciences and Engineering 6.7 (2018): 1478-1484.

APA Style Citation: S. Raheja, R. Singh, (2018). Test case selection using multi-objective Evolutionary Algorithms. International Journal of Computer Sciences and Engineering, 6(7), 1478-1484.

BibTex Style Citation:
@article{Raheja_2018,
author = {S. Raheja, R. Singh},
title = {Test case selection using multi-objective Evolutionary Algorithms},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {7 2018},
volume = {6},
Issue = {7},
month = {7},
year = {2018},
issn = {2347-2693},
pages = {1478-1484},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=2630},
doi = {https://doi.org/10.26438/ijcse/v6i7.14781484}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v6i7.14781484}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=2630
TI - Test case selection using multi-objective Evolutionary Algorithms
T2 - International Journal of Computer Sciences and Engineering
AU - S. Raheja, R. Singh
PY - 2018
DA - 2018/07/31
PB - IJCSE, Indore, INDIA
SP - 1478-1484
IS - 7
VL - 6
SN - 2347-2693
ER -

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Abstract

Regression testing is needed to ensure the correct behavior of software after change. For the process of automation and selection of test cases, a number of meta-heuristic techniques have been used in literature. In this paper, bat algorithm, cuckoo search and multi-objective binary genetic algorithms have been discussed. The proposed multi-objective binary genetic algorithm is evaluated against test functions and its performance is analyzed in comparison to existing algorithms i.e. bat and cuckoo search algorithm. For this, we have considered factors such as fault coverage and execution time. The related dataset is extracted from benchmark repository named flex object which originates from SIR. Results indicate that multi-objective performs better than bat and cuckoo search algorithm.

Key-Words / Index Term

Software Testing, Regression Testing, Bat Algorithm, Cuckoo Search Algorithm, Software Maintenance

References

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