Genetic Algorithm Approach For Test Case Generation Randomly: A Review

International Journal of Computer Trends and Technology (IJCTT)          
© 2017 by IJCTT Journal
Volume-49 Number-4
Year of Publication : 2017
Authors : Deepak kumar, Manu Phogat
DOI :  10.14445/22312803/IJCTT-V49P134


Deepak kumar, Manu Phogat "Genetic Algorithm Approach For Test Case Generation Randomly: A Review". International Journal of Computer Trends and Technology (IJCTT) V49(4):213-216, July 2017. ISSN:2231-2803. Published by Seventh Sense Research Group.

Abstract -
The quality of software is dependent on testing as per user specifications and requirements. So it is quite challenging to design, prioritize and optimize test cases to achieve quality. Different testing tools can be used for software testing either manually or automatically. During the recent studies it is found that automated software testing is better than manual testing by using heuristic search. In this paper presents a survey on genetic algorithm approach for random generation of test cases in functional software testing.

[1] Jones,C., Bonsignour, O.: The Economics of Software Quality, Pearson Education Inc., 2012
[2] Shivani, A., and Pandya. V., Bridge between Black Box and White Box–Gray Box Testing Technique, International Journal of Electronics and Computer Science Engineering 2.1 (2012): 175-185.
[3] Chauhan, N., Software Testing: Principles and Practices, Oxford University Press, 2010.
[4] Jogersen, P. C., Software testing: A craftsman approach, 3rd edition, CRC presses, 2008.
[5] Shahbazi, A., and Miller, J., Black-Box String Test Case Generation Through A Multi-Objective Optimization, IEEE Transactions On Software Engineering, 42(4), 2016
[6] Michael, C.C , McGraw, G. E., Schatz, M. A. and Walton, C.C., Genetic Algorithms for Dynamic Test Data Generation, Proceedings of the 1997 International Conference on Automated Software Engineering (ASE`97) (formerly: KBSE) 1997.
[7] Doungsaard, C., Dahal, K., Hossain, A., Suwannasart, T., Test data generation from UML state machine diagrams using GAs, International Conference on Software Engineering Advances (ICSEA 2007). IEEE, 2007.
[8] Srivastava, P.P., and Kim, T., Application of genetic algorithm in software testing, International Journal of software Engineering and its Applications, Vol.3, No.4, pp.87 – 96, 2009.
[9] Berndt, D.J., and Watkins, A., High volume software testing using genetic algorithms, Proceedings of the 38th Annual Hawaii International Conference on System Sciences Washington, DC, USA: IEEE Computer Society, Vol. 9, pp. 318–326, 2005.
[10] Dixit, S., and Tomar, P., Automated test data generation using computational intelligence, Reliability, Infocom Technologies and Optimization (ICRITO)(Trends and Future Directions), 4th International Conference on. IEEE, 2015.
[11] Sharma, A., Patani , R. ,and Aggarwal, A., Software Testing using Genetic Algorithms, International Journal of Computer Science & Engineering Survey Vol.7, No.2, 2016
[12] Moataz A. A, and Ali, F., Multiple-path testing for cross site scripting using genetic algorithms, Journal of Systems Architecture Vol.64, pp.50-62, 2016.
[13] Yang, S., Man, T., Xu, J., Zeng, F., Li, K., RGA: A lightweight and effective regeneration genetic algorithm for coverage-oriented software test data generation, Information and Software Technology, Vol.76, pp.19-30, 2016.
[14] Last, M., Eyal, S., Effective black-box testing with genetic algorithms, Lecture notes in computer science, Springer,pp. 134 -148, 2006.
[15] Peng, X., & Lu, L., A new approach for session-based test case generation by GA. In Communication Software and Networks (ICCSN),IEEE 3rd International Conference on IEEE, pp. 91-96, 2011.
[16] Zhao, R., lv, S., Neural network based test cases generation using genetic algorithm, 13th IEEE international symposium on Pacific Rim dependable computing. IEEE, pp.97 – 100, 2007.
[17] Srivastava, P.R., and Kim, T.H ., Application of genetic algorithm in software testing, International Journal of software Engineering and its Applications, Vol.3, No.4, pp.87 – 96, 2009.
[18] Riberio, J.C.B., Zenha-Rela, M.A., De vega, F.F., A strategy for evaluating feasible and unfeasible test cases for the evolutionary testing of object oriented software”, AST’ 08. ACM, 2008.
[19] Goldberg, D.E., Genetic Algorithms: In search, optimization and machine learning, Addison Wesley, M.A, 1989.
[20] Wappler, S., Lammermann, F., Using evolutionary algorithms for unit testing of object oriented software, GECCO. ACM, pp.1925 – 1932, 2005.
[21] Vieria, F. E., Martins, F., Silva, R., Menezes, R., Braga., M., Using Genetic algorithms for test plans for functional testing, 44th ACM SE proceeding, pp.140 – 145,2006.
[22] Mathur, A.P., Foundation of Software Testing, 1st edition Pearson Education 2008.
[23] Rauf, A., Anwar, S., Jaffer, M. A., & Shahid, A. A., Automated GUI test coverage analysis using GA, In Information Technology: New Generations (ITNG), Seventh International Conference , IEEE, pp. 1057-1062, 2010.
[24] Andalib, A., and Babamir, S.M., A New Approach for Test Case Generation by Discrete Particle Swarm Optimization Algorithm, The 22nd Iranian Conference on Electrical Engineering, 2014.
[25] Zhao, R., & Lv, S., Neural-network based test cases generation using genetic algorithm. In Dependable Computing, 13th Pacific Rim International Symposium IEEE, pp. 97-100, 2007
[26] Li, K., Zhang, Z., Kou, J., Breeding Software Data with Genetic Particle Swarm Mixed Algorithm, Journal of Computers, Vol.5, No.2,pp. 074-085, 2010.

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