Research Article
Implementation and Analysis of the Bee Colony Optimization algorithm for Fault based Regression Test Suite Prioritization
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 41 - Issue 14 |
| Published: March 2012 |
| Authors: Arvinder Kaur, Shivangi Goyal |
10.5120/5606-7867
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Arvinder Kaur, Shivangi Goyal . Implementation and Analysis of the Bee Colony Optimization algorithm for Fault based Regression Test Suite Prioritization. International Journal of Computer Applications. 41, 14 (March 2012), 1-9. DOI=10.5120/5606-7867
@article{ 10.5120/5606-7867,
author = { Arvinder Kaur,Shivangi Goyal },
title = { Implementation and Analysis of the Bee Colony Optimization algorithm for Fault based Regression Test Suite Prioritization },
journal = { International Journal of Computer Applications },
year = { 2012 },
volume = { 41 },
number = { 14 },
pages = { 1-9 },
doi = { 10.5120/5606-7867 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2012
%A Arvinder Kaur
%A Shivangi Goyal
%T Implementation and Analysis of the Bee Colony Optimization algorithm for Fault based Regression Test Suite Prioritization%T
%J International Journal of Computer Applications
%V 41
%N 14
%P 1-9
%R 10.5120/5606-7867
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Regression Testing is an important maintenance phase testing activity. The importance of this activity lies in the fact that it imparts confidence and accuracy in the modified code, as well as keeps a check on the unmodified parts, if they are affected or not. But there is a severe requirement to reorder the development testing test suite because of the constrained software development budget, time and effort. So techniques have to be developed to prioritize test cases to reduce budget, time and effort constraints effectively. In this paper implementation and analysis of an existing fault based regression test suite has been done. The prioritization algorithm is based on the nature inspired algorithm called Bee Colony Optimization (BCO) algorithm. The algorithm is a two step procedure which maps the food foraging behavior of scout bee and forager bee one after the other to reach to the solution. The analysis of the examples using the code developed indicates that the two step BCO algorithm is able to produce results which are comparable to optimal results.
References
- Kaur, A. and Goyal, S. , 2011. A Bee Colony Optimization Algorithm for Fault Coverage Based Regression Test Suite Prioritization, IJAST, Vol. 29(3), pp. :17-29, April 2011.
- Kaur, A. and Goyal, S. , 2011. A Bee Colony Optimization Algorithm for Code Coverage Test Suite Prioritization. IJEST, Vol. 3(4), pp. :2786-2795. April 2011.
- Rothermel, G. Untch, R. H. , Chu, C. , and Horrold, M. J. , 1999. Test Case Prioritization: An Empirical Study, Proceedings of the International Conference on Software Maintenance, pp. : 179-188, September 1999.
- Walcott , K. R. ,So, M. L. , Kapfhammer, G. M. , and Roos, R. S. ,2006. Time aware test suite prioritization, ISSTA, pp. : 1-11, 2006.
- Askarunisa, A. , Shanmugapriya, L. , and Ramaraj, N. , 2009. Cost and Coverage Metrics for Measuring the Effectiveness of Test Case Prioritization Techniques, INFOCOMP Journal of Computer Science, pp. 1-10, 2009.
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