Research Article | OPEN ACCESS
Fuzzy C Means (FCM) Clustering Based Hybrid Swarm Intelligence Algorithm for Test Case Optimization
1Abraham Kiran Joseph and 2G. Radhamani
1Dr. G.R. Damodaran College of Science
2Department of Computer Science, Dr. G.R. Damodaran College of Science,
Affiliated to Bharathiar University, Tamilnadu, India
Research Journal of Applied Sciences, Engineering and Technology 2014 1:76-82
Received: March ‎02, ‎2014 | Accepted: April ‎17, ‎2014 | Published: July 05, 2014
Abstract
The main objective of an operative testing strategy is the delivery of a reliable and quality oriented software product to the end user. Testing an application entirely from end to end is a time consuming and laborious process. Exhaustive testing utilizes a good chunk of the resources in a project for meticulous scrutiny to identify even a minor bug. A need to optimize the existing suite is highly recommended, with minimum resources and a shorter time span. To achieve this optimization in testing, a technique based on combining Artificial Bee Colony algorithm (ABC) integrated with Fuzzy C-Means (FCM) and Particle Swarm Optimization (PSO) is described here. The initiation is done with the ABC algorithm that consists of three phases-the employed bee, the onlooker bee and the scout bee phase. The artificial bees that are initialized in the ABC algorithm identify the nodes with the highest coverage. This results in the ABC algorithm generating an optimal number of test-cases, which are sufficient to cover the entire paths within the application. The node with the highest usage by a given test case is determined by the PSO algorithm. Based on the above ‘hybrid’ optimization approach of ABC and PSO algorithms, a set of test cases that are optimal are obtained by repeated pruning of the original set of test cases. The performance of the proposed method is evaluated and is compared with other optimization techniques to emphasize the fact of improved quality and reduced complexity.
Keywords:
Hybrid swarm intelligence algorithm, optimization algorithm, software testing , test cases,
References
-
Al-Tabtabai, H. and P.A. Alex, 1999. Using genetic algorithms to solve optimization problems in construction. Eng. Constr. Archit. Manage., 6(2): 121-32.
CrossRef -
Bansal, P., 2013. A critical review on test case prioritization and optimization using soft computing techniques. Proceeding of 2nd International Conference on Role of Technology in Nation Building (ICRTNB, 2013), ISBN: 97881925922-1-3.
-
Binder, R.V., 2000. Testing Object-oriented Systems: Models, Patterns and Tools. Addison-Wesley. Reading, Mass.
-
Clarke, L.A., 1976. A system to generate test data and symbolically execute programs. IEEE T. Software Eng., SE-2(3): 215-222.
CrossRef -
Dervis, K. and C. Ozturk, 2011. A novel clustering approach: Artificial Bee Colony (ABC) algorithm. Appl. Soft Comput., 11: 652-657.
CrossRef -
Doungsaard, C., K. Dahal, A. Hossain and T. Suwannasart, 2008. An automatic test data generation from UML state diagram using genetic algorithm. Supported by the EU Asia-link project - TH/Asia Link/004 (91712) -Euro-Asia Collaboration and Networking in Information Engineering System Technology 2008 (EAST-WEST).
-
Fabrício, G.D.F., L.B.M. Camila, L.D.C. Gustavo Augusto and T.D.S. Jerffeson, 2010. Optimization in software testing using metaheuristics. Revista de Sistemas de Informação da FSMA n., 5(2010): 3-13.
-
Kaur, A. and S. Goyal, 2011. A bee colony optimization algorithm for fault coverage based regression test suite prioritization. Int. J. Adv. Sci. Technol., Vol. 29.
-
Korel, B., 1990. Automated software test data generation. IEEE T. Software Eng., 10(8): 870-879.
CrossRef -
Krishnamoorthi, R. and S.A. Sahaaya Arul Mary, 2009. Regression test suite prioritization using genetic algorithms. Int. J. Hybrid Inform. Technol., 2(3).
-
Krishnamoorthi, M. and A.M. Natarajan, 2013. Artificial bee colony algorithm integrated with fuzzy C-mean operator for data clustering. J. Comput. Sci., 9(4): 404-412.
CrossRef -
Li, H. and C.P. Lam, 2011. An Ant Colony Optimization Approach to Test Sequence Generation for State based Software Testing. ECU Publications Pre, 2011.
-
Mahapatra, R.P. and J. Singh, 2008. Improving the effectiveness of software testing through test case reduction. Proceedings of World Academy of Science, Engineering and Technology, 2, ISSN: 1307-6884.
-
Pressman, R.S., 2007. Software engineering: A practitioners Approach. 6th Edn., Ch. 1(33-47), 13(387-406), 14(420-444). McGraw-Hill, New York.
PMCid:PMC1978391 -
Reid, S.C., 1997. An empirical analysis of equivalence partitioning, boundary value analysis and random testing. Proceedings of the 4th International on Software Metrics Symposium. Albuquerque, NM, USA, pp: 64-73.
CrossRef PMid:9034505 -
Rothermel, G., M.J. Harrold, J. Ostrin and C. Hong, 1998. An empirical study of the effects of minimization on the fault detection capabilities of test suites. Proceedings of the International Conference on Software Maintenance, pp: 34-43.
CrossRef -
Shi, Y. and R. Eberhart, 1998. A modified particle swarm optimizer. Proceedings of the IEEE International Conference on Evolutionary Computation, pp: 69-73.
CrossRef -
Sonmez, Y., 2011. Multi-objective Environmental/ economic dispatch solution with penalty factor using artificial bee colony algorithm. Sci. Res. Essays, 6(13): 2824-2831.
-
Srikanth, A., N.J. Kulkarni, K.V. Naveen, P. Singh and P.R. Srivastava, 2011. Test Case Optimization using Artificial Bee Colony Algorithm. In: Abraham, A. (Ed.), ACC 2011, Part III, CCIS 192. Springer-Verlag, Berlin, Heidelberg, pp: 570-579.
-
Zhong, H., L. Zhang and H. Mei, 2003. An experimental study of four typical test suite reduction techniques. Inform. Software Tech., 50(6): 534-546.
CrossRef
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
|
|
![](http://www.maxwellsci.com/images/RJASET-maxw.jpg) |
ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
![Submit Manuscript](../images/sub.jpg) |
Information |
|
|
|
Sales & Services |
|
|
|