A REVIEW ON SOFTWARE PATH TESTING USING EVOLUTIONARY ALGORITHMS

DEEPTI Bala MISHRA, Arup Abhinna Acharya, Rajashree MISHRA

Abstract


Software testing is very time consuming, labor-intensive and complex process. It is found that 50% of the resources of the software development are consumed for software testing. Testing can be done in two different ways such as manual testing and automatic testing. Path testing is the strongest coverage criteria among all white box testing techniques as it can detect about 65% of defects in a SUT. With the help of path testing, the test cases are created and executed for all possible paths which result in 100% statement coverage and 100% branch coverage. This paper presents a systematic review of test data generation and optimization for path testing using evolutionary algorithms.


Keywords


Path Testing; Genetic Algorithm (GA); Particle Swarm Optimization (PSO); Ant Colony Optimization (ACO); Artificial Bee Colony (ABC);

References


Mathur, A.P., 2013. Foundations of Software Testing, 2/e. Pearson Education India.

Mishra, D.B., Bilgaiyan, S., Mishra, R., Acharya, A.A. and Mishra, S., 2017. A Review of Random Test Case Generation using Genetic Algorithm. Indian Journal of Science and Technology, 10(30).

Chauhan, N., Software Testing: Principles and Practices, Oxford University Press, 2010.

Srivastava, P.R. and Kim, T.H., 2009. Application of genetic algorithm in software testing. International Journal of software Engineering and its Applications, 3(4), pp.87-96.

Ahmed, M.A. and Hermadi, I., 2008. GA-based multiple paths test data generator. Computers & Operations Research, 35(10), pp.3107-3124.

Zhang, S., Zhang, Y., Zhou, H. and He, Q., 2010, October. Automatic path test data generation based on GA-PSO. In Intelligent Computing and Intelligent Systems (ICIS), 2010 IEEE International Conference on (Vol. 1, pp. 142-146). IEEE.

Zhang, Y. and Gong, D., 2014. Generating test data for both paths coverage and faults detection using genetic algorithms: multi-path case. Frontiers of Computer Science, 8(5), pp.726-740.

Mohi-Aldeen, S.M., Mohamad, R. and Deris, S., 2016. Application of Negative Selection Algorithm (NSA) for test data generation of path testing. Applied Soft Computing, 49, pp.1118-1128.

Sharma, A., Rishon, P. and Aggarwal, A., 2016. Software testing using genetic algorithms. Int. J. Comput. Sci. Eng. Surv.(IJCSES), 7(2), pp.21-33.

Juristo, N., Moreno, A.M. and Vegas, S., 2004. Reviewing 25 years of testing technique experiments. Empirical Software Engineering, 9(1-2), pp.7-44.

Manikumar, T., Kumar, A.J.S. and Maruthamuthu, R., 2016. Automated test data generation for branch testing using incremental genetic algorithm. Sādhanā, 41(9), pp.959-976.

Alshraideh, M., Mahafzah, B.A. and Al-Sharaeh, S., 2011. A multiple-population genetic algorithm for branch coverage test data generation. Software Quality Journal, 19(3), pp.489-513.

Latiu, G.I., Cret, O.A. and Vacariu, L., 2012, September. Automatic test data generation for software path testing using evolutionary algorithms. In Emerging Intelligent Data and Web Technologies (EIDWT), 2012 Third International Conference on (pp. 1-8). IEEE.

Khari, M. and Kumar, P., 2017. An extensive evaluation of search-based software testing: a review. Soft Computing, pp.1-14.

Mansour, N. and Salame, M., 2004. Data generation for path testing. Software Quality Journal, 12(2), pp.121-136.

Gupta, M. and Gupta, G., 2012. Effective test data generation using genetic algorithms. Journal of Engineering, Computers & Applied Sciences, 1(2), pp.17-21.

Hermadi, I., Lokan, C. and Sarker, R., 2010, December. Genetic algorithm based path testing: challenges and key parameters. In Software Engineering (WCSE), 2010 Second World Congress on (Vol. 2, pp. 241-244). IEEE.

Shahbazi, A. and Miller, J., 2016. Black-box string test case generation through a multi-objective optimization. IEEE Transactions on Software Engineering, 42(4), pp.361-378.

Yan, J. and Zhang, J., 2008. An efficient method to generate feasible paths for basis path testing. Information Processing Letters, 107(3-4), pp.87-92.

Yang, S., Man, T., Xu, J., Zeng, F. and Li, K., 2016. RGA: A lightweight and effective regeneration genetic algorithm for coverage-oriented software test data generation. Information and Software Technology, 76, pp.19-30.

Zapata, F., Akundi, A., Pineda, R. and Smith, E., 2013. Basis path analysis for testing complex system of systems. Procedia Computer Science, 20, pp.256-261.

Han, X., Lei, H. and Wang, Y.S., 2016. Multiple paths test data generation based on particle swarm optimization. IET Software, 11(2), pp.41-47.

Lin, J.C. and Yeh, P.L., 2000. Using genetic algorithms for test case generation in path testing. In Test Symposium, 2000.(ATS 2000). Proceedings of the Ninth Asian (pp. 241-246). IEEE.

Chen, Y. and Zhong, Y., 2008, October. Automatic path-oriented test data generation using a multi-population genetic algorithm. In Natural Computation, 2008. ICNC'08. Fourth International Conference on (Vol. 1, pp. 566-570). IEEE.

Shimin, L. and Zhangang, W., 2011. Genetic Algorithm and its Application in the path-oriented test data automatic generation. Procedia Engineering, 15, pp.1186-1190.

Hermadi, I. and Ahmed, M.A., 2003, December. Genetic algorithm based test data generator. In Evolutionary Computation, 2003. CEC'03. The 2003 Congress on (Vol. 1, pp. 85-91). IEEE.

Goldberg, D.E., 1991. Real-coded genetic algorithms, virtual alphabets, and blocking. Complex systems, 5(2), pp.139-167.

Mishra, B.S.P., Dehuri, S., Mall, R. and Ghosh, A., 2013. Parallel single and multiple objectives genetic algorithms: A survey. In Modeling Applications and Theoretical Innovations in Interdisciplinary Evolutionary Computation (pp. 73-110). IGI Global.

Bilgaiyan, S., Mishra, S. and Das, M., 2016, January. A review of software cost estimation in agile software development using soft computing techniques. In Computational Intelligence and Networks (CINE), 2016 2nd International Conference on (pp. 112-117). IEEE.

Zhu, Z., Xu, X. and Jiao, L., 2017, June. Improved evolutionary generation of test data for multiple paths in search-based software testing. In Evolutionary Computation (CEC), 2017 IEEE Congress on (pp. 612-620). IEEE.

Deep, K. and Das, K.N., 2013. A novel hybrid genetic algorithm for constrained optimization. International Journal of System Assurance Engineering and Management, 4(1), pp.86-93.

Das, K.N. and Mishra, R., 2013. Chemo-inspired genetic algorithm for function optimization. Applied Mathematics and Computation, 220, pp.394-404.

Deb, K., 2012. Optimization for engineering design: Algorithms and examples. PHI Learning Pvt. Ltd..

Jena, T. and Mohanty, J.R., 2016. Disaster recovery services in intercloud using genetic algorithm load balancer. International Journal of Electrical and Computer Engineering, 6(4), p.1828.

Mishra, D.B., Mishra, R., Das, K.N. and Acharya, A.A., 2017. A Systematic Review of Software Testing Using Evolutionary Techniques. In Proceedings of Sixth International Conference on Soft Computing for Problem Solving (pp. 174-184). Springer, Singapore.

Jones, B.F., Sthamer, H.H. and Eyres, D.E., 1996. Automatic structural testing using genetic algorithms. Software Engineering Journal, 11(5), pp.299-306.

Ahmed, M.A. and Hermadi, I., 2008. GA-based multiple paths test data generator. Computers & Operations Research, 35(10), pp.3107-3124.

Zhonglin, Z. and Lingxia, M., 2010, August. An improved method of acquiring basis path for software testing. In Computer Science and Education (ICCSE), 2010 5th International Conference on (pp. 1891-1894). IEEE.

Lam, S.S.B., Raju, M.H.P., Ch, S. and Srivastav, P.R., 2012. Automated generation of independent paths and test suite optimization using artificial bee colony. Procedia Engineering, 30, pp.191-200.

Boopathi, M., Sujatha, R., Kumar, C.S. and Narasimman, S., 2014, October. The mathematics of software testing using genetic algorithm. In Reliability, Infocom Technologies and Optimization (ICRITO)(Trends and Future Directions), 2014 3rd International Conference on (pp. 1-6). IEEE.

Ghiduk, A.S., 2014. Automatic generation of basis test paths using variable length genetic algorithm. Information Processing Letters, 114(6), pp.304-316.

Huang, M., Zhang, C. and Liang, X., 2014, December. Software test cases generation based on improved particle swarm optimization. In Information Technology and Electronic Commerce (ICITEC), 2014 2nd International Conference on(pp. 52-55). IEEE.

Garg, D. and Garg, P., 2015. Basis Path Testing Using SGA & HGA with ExLB Fitness Function. Procedia Computer Science, 70, pp.593-602.

Biswas, S., Kaiser, M.S. and Mamun, S.A., 2015, May. Applying Ant Colony Optimization in software testing to generate prioritized optimal path and test data. In Electrical Engineering and Information Communication Technology (ICEEICT), 2015 International Conference on (pp. 1-6). IEEE.

Yao, X., Gong, D. and Wang, W., 2015. Test data generation for multiple paths based on local evolution. Chinese Journal of Electronics, 24(1), pp.46-51.

R. Khan, M. Amjad and A. K. Srivastava, "Optimization of Automatic Generated Test Cases for Path Testing Using Genetic Algorithm," 2016 Second International Conference on Computational Intelligence & Communication Technology (CICT), Ghaziabad, 2016, pp. 32-36.

Thi, D.N., Hieu, V.D. and Ha, N.V., 2016, November. A Technique for Generating Test Data Using Genetic Algorithm. In Advanced Computing and Applications (ACOMP), 2016 International Conference on (pp. 67-73). IEEE.

Khari, M., Kumar, P., Burgos, D. and Crespo, R.G., 2017. Optimized test suites for automated testing using different optimization techniques. Soft Computing, pp.1-12.




DOI: http://doi.org/10.11591/ijeecs.v15.i1.pp%25p
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