Abstract
Test case optimization is one of the techniques which efficiently manage the exponential growth in time and cost of testing. But in many times the researchers compromise with the code coverage while going for optimization. In this paper, the test suite is optimized using Intelligent Optimization Agent (IOA) while the keeping the percentage of code coverage unchanged. First the System Under Test (SUT) is modelled using UML Activity Diagram (AD) and converted into an Activity Graph (AG). Then the optimized path is found out in AD by using IOA and cost attributes. Then suitable algorithms are proposed to remove the redundant nodes in the optimized path. IOA is an agent based approach as compared to Hybrid Genetic Algorithm (HGA) in Intelligent Test Optimization Agent (ITOA).The proposed approach is found to be effective when compared with other optimization techniques like Genetic Algorithm (GA) and Intelligent Test Optimization Agent (ITOA).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alshraideh, M.A., Mahafzah, B.A., Salman, H.S.E., Salah, I.: Using genetic algorithm as test data generator for stored PL/SQL program units. J. Softw. Eng. Appl. 6, 65–73 (2013)
de Souza, L.S., de Miranda, P.B.C., Prudencio, R.B.C., de Barros, F.A.: A multi-objective particle swarm optimization for test case selection based on functional requirements coverage and execution effort. In: 23rd IEEE International Conference on Tools with Artificial Intelligence (2011)
Han, X., Zeng, H., Gao, H.: A heuristic model-based test prioritization method for regression testing. In: International Symposium on Computer, Consumer and Control, pp. 886–889. IEEE (2012)
Mahali, P., Acharya, A.A.: Model based test case prioritization using UML activity diagram and evolutionary algorithm. Int. J. Comput. Sci. Inform. 3, 42–47 (2013)
Mala, D., Mohan, V.: Intelligentester-software test sequence optimization using graph based intelligent search agent. In: International Conference on Computational Intelligence and Multimedia Applications, pp. 22–27 (2007)
Mala, D., Mohan, V.: Intelligentester-test sequence optimization framework using multi-agents. J. Comput. 3(6), 39–46 (2008)
Mall, R.: Fundamental of Software Engineering. PHI Learning Private Limited, New Delhi (2009)
Rothermal, G., Untch, R.H., Chu, C., HarRold, M.J.: Prioritizing test cases for regression testing. IEEE Trans. Softw. Eng. (2001)
Singhal, A., Chandna, S., Bansal, A.: Optimization of test cases using genetic algorithm. Int. J. Emerg. Technol. Adv. Eng. 2, 367–369 (2012)
Srikanth, A., Kulkarni, N.J., Naveen, K.V., Singh, P., Srivastava, P.R.: Test case optimization using artificial bee colony algorithm, pp. 570–579. Springer, Berlin (2011)
Suman, S.: A genetic algorithm for regression test sequence optimization. Int. J. Adv. Res. Comput. Commun. Eng. 1 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer India
About this paper
Cite this paper
Mahali, P., Acharya, A.A., Mohapatra, D.P. (2015). Model Based Test Case Generation and Optimization Using Intelligent Optimization Agent. In: Mandal, J., Satapathy, S., Kumar Sanyal, M., Sarkar, P., Mukhopadhyay, A. (eds) Information Systems Design and Intelligent Applications. Advances in Intelligent Systems and Computing, vol 339. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2250-7_47
Download citation
DOI: https://doi.org/10.1007/978-81-322-2250-7_47
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-2249-1
Online ISBN: 978-81-322-2250-7
eBook Packages: EngineeringEngineering (R0)