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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

[1] S Nachiyappan, A Vimala devi and C B Selva Lakshmi, “An evolutionary algorithm for regression test suite reduction”, In Communication and Computational Intelligence (INCOCCI), 2010 International Conference, pp. 503-508, 2010, December, IEEE.
[2] G Rothermel , S Elbaum, A Kinneer and H Do, 2006. Software-artifact infrastructure repository. URL http:// sir.unl. edu/portal.
[3] R.Y Nakamura, L.A Pereira, K A Costa, D Rodrigues, J.P Papa and X.S Yang, 2012,“BBA: a binary bat algorithm for feature selection”. In 2012, 25th SIBGRAPI Conference on Graphics Patterns and Images, pp. 291-297, 2012,August, IEEE.
[4] Zhaolu Guo, Xuezhi Yue, Kejun Zhang, Shenwen Wang and Zhijian Wu, “A Thermodynamical Selection-Based Discrete Differential Evolution for the 0-1 Knapsack Problem”, Entropy 2014, 16, 6263-6285; doi:10.3390/e16126263.
[5] Khan, K. and Sahai, A., 2012. A comparison of BA, GA, PSO, BP and LM for training feed forward neural networks in e-learning context. International Journal of Intelligent Systems and Applications ,4(7), p.23.
[6] Songwei Huang, Lifang He, Xu Si, Yuanyuan Zhang and Pengyu Hao, An Effective Krill Herd Algorithm for Numerical Optimization, International Journal of Hybrid Information Technology Vol. 9, No.7 (2016), pp. 127-138 http://dx.doi.org/10.14257/ijhit.2016.9.7.13.
[7] Usha Badhera, G.N Purohit, Debarupa Biswas, 2012 Test case prioritization algorithm based upon modified code coverage regression testing, International Journal of Software Engineering & Applications (IJSEA), Vol.3, No.6, November 2012.
[8] Luciano S. de Souza, Ricardo B.C. Prudêncio, Flavia de A. Barros, Eduardo H. da S. Aranha, 2013 Search based constrained test case selection using execution effort, Expert Systems with Applications 40 (2013) 4887–4896.
[9] Yanhong Feng, Ke Jia and Yichao He, An Improved Hybrid Encoding Cuckoo Search Algorithm for 0-1 Knapsack Problems, Hindawi Publishing Corporation Computational Intelligence and Neuroscience Volume 2014, Article ID 970456, 9 pages http://dx.doi.org/10.1155/2014/970456.
[10] Srivastava P.R, Sravya C, Ashima, Kamisetti S and Lakshmi M, 2012. Test sequence optimisation: an intelligent approach via cuckoo search. International Journal of Bio-Inspired Computation,4(3), pp.139-148.
[11] Nagar R, Kumar A, Singh G.P and Kumar S, 2015, February. Test case selection and prioritization using cuckoos search algorithm. In Futuristic Trends on Computational analysis and knowledge management(ABLAZE) 2015, International Conference on (pp. 283-288).IEEE.
[12] Yang, X.S., 2010. A new metaheuristic bat-inspired algorithm. In Nature Inspired Cooperative Stratergies for Optimization (NICSO 2010) (pp. 65-74). Springer Berlin Heidelberg.
[13] Biswal, S., Barisal, A.K., Behera, A. and Prakash, T., 2013, April. Optimal power dispatch using BAT algorithm.InEnergy Efficient Technologies for Sustainability (ICEETS) 2013, National Conference on(pp. 1018-1023). IEEE.
[14] Yang, X.S. and Deb, S., 2009, December. Cuckoo search via Lévy flights. In Nature and Biologically Inspired Computing, 2009 NaBIC 2009, World Congress on (pp. 210-214). IEEE.
[15] Deb, K., 2001, “Multi-Objective Optimization using Evolutionary Algorithms,” Wiley Chichester, UK.
[16] Abdullah Konak, David W. Coit , Alice E. Smith, Multi-objective optimization using genetic algorithms: A tutorial, Reliability Engineering & System Safety Volume 91, Issue 9, September 2006, Pages 992-1007, Elsevier.
[17] P. Sudheer Kumar Reddy* P. Anil Kumar G.N.S. Vaibhav, Application of BAT Algorithm for Optimal Power Dispatch, International Journal of Innovative Research in Advanced Engineering (IJIRAE) ISSN: 2349-2163 Issue 2, Volume 2 (February 2015).
[18] Lingzhi Yi, Yue Liu, wenxin Yu, Genping Wang, Yongbo Sui, Adaptive Cuckoo Search Algorithm for the Speed Control System of Induction Motor, SCIREA Journal of Electrical Engineering http://www.scirea.org/journal/DEE February 21, 2017 Volume 2, Issue 1, February 2017.
[19] De Souza, L.S., Prudêncio, R.B. and Barros, F.D.A., MultiObjective Test Case Selection: A study of the influence of the Catfish effect on PSO based strategies.
[20] Ali, A., Nadeem, A., Iqbal, M.Z.Z. and Usman, M., 2007, December. Regression testing based on UML design models. In Dependable Computing, 2007. PRDC 2007. 13th Pacific Rim International Symposium on (pp. 85-88). IEEE.
[21] Gupta Nirmal Kumar and Rohil Mukesh Kumar "Improving GA based Automated Test Data Generation Technique for Object Oriented Software", IEEE International Advance Computing Conference (IACC), Ghaziabad, pp.249 – 253, 2013.
[22] Srivastava, P.R., Bidwai, A., Khan, A., Rathore, K., Sharma, R. and Yang, X.S., 2014. An empirical study of test effort estimation based on bat algorithm. International Journal of Bio-Inspired Computation,6(1), pp.57-70.
[23] Praveen Ranjan Srivastava, Chandolu Sravya, Ashima, Sai Kamisetti and Manogna Lakshmi, Test sequence optimisation: an intelligent approach via cuckoo search, Int. J. Bio-Inspired Computation, Vol. 4, No. 3, 2012.
[24] Ehsan Valian, Saeed Tavakoli, Shahram Mohanna, Atiyeh Haghi, Improved cuckoo search for reliability optimization problems, Computers & Industrial Engineering 64 (2013) 459–468, Elsevier.
[25] Xin-She Yang, Bat algorithm: literature review and applications, Int. J. Bio-Inspired Computation, Vol. 5, No. 3, pp. 141–149 (2013). DOI: 10.1504/IJBIC.2013.055093
[26] S. L. Yadav, M. Phogat, “A Review on Bat Algorithm”, International Journal of Computer Sciences and Engineering, Volume-5, Issue-7, E-ISSN: 2347-2693.