Regression Test Suite Minimization Using Modified Artificial Ecosystem Optimization Algorithm

Document Type : Special Issue on Pragmatic Approaches of Software Engineering for Big Data Analytics, Applications and Development

Authors

1 Assistant Professor, CSED, School of Engineering & Technology, Sharda University, Greater Noida, India.

2 Professor, Department of Computer Science & Engineering, Sharda University, Greater Noida, India.

3 Professor, Department of Computer Science & Engineering, ABES Engineering College, Ghaziabad, India.

Abstract

Now a day's software is the baseline for the success of any organization. There is a huge demand of quality software in the customer-oriented market. Regression testing makes it possible but it’s a costly affair. Regression test suite minimization is way to reduce this cost but it is NP hard problem. This paper proposes an effective approach for regression test suite minimization using Artificial Ecosystem Optimization algorithm. To improve its performance a modified Artificial Ecosystem Optimization algorithm is proposed for Test case minimization. To evaluate the performance of proposed approach experiment is conducted in controlled parameter setting on open-source subject program from SIR repository. The results are collected and analyzed in comparison to existing approaches using statistical test. The test results reflect the superiority of proposed approach.

Keywords


Agrawal, A. P., Choudhary, A., Kaur, A., & Pandey, H. M. (2019). Fault coverage-based test suite optimization method for regression testing: learning from mistakes-based approach. Neural Computing and Applications, February. https://doi.org/10.1007/s00521-019-04098-9
Ahmad Khan, F., Bora, D. J., & Gupta, A. K. (2017). An Efficient Heuristic Based Test Suite Minimization Approach. Indian Journal of Science and Technology, 10(29), 1–8. https://doi.org/10.17485/ijst/2017/v10i29/106374
Ahmed, B. S. (2015). Test case minimization approach using fault detection and combinatorial optimization techniques for configuration-aware structural testing. Engineering Science and Technology, an International Journal, 6(2), 189–213. https://doi.org/10.1007/s10479-005-3971-7
Augustsson, A. (2012). A Framework for Evaluating Regression Test Selection Techniques in Industry. Proceedings of 16th International Conference on Software Engineering, April, 201–210.
Black, J., Melachrinoudis, E., & Kaeli, D. (2004). Bi-criteria models for all-uses test suite reduction. Proceedings. 26th International Conference on Software Engineering, 106–115. https://doi.org/10.1109/ICSE.2004.1317433
Chaudhary, A., Agarwal, A. P., Rana, A., & Kumar, V. (2019). Crow Search Optimization Based Approach for Parameter Estimation of SRGMs. Proceedings - 2019 Amity International Conference on Artificial Intelligence, AICAI 2019, 583–587. https://doi.org/10.1109/AICAI.2019.8701318
Chen, T. Y., & Lau, M. F. (1998a). A new heuristic for test suite reduction. Information and Software Technology, 40(5–6), 347–354. https://doi.org/10.1016/S0950-5849(98)00050-0
Chen, T. Y., & Lau, M. F. (1998b). A simulation study on some heuristics for test suite reduction. Information and Software Technology, 40(13), 777–787. https://doi.org/10.1016/S0950-5849(98)00094-9
Chen, T. Y., & Lau, M. F. (2003). On the divide-and-conquer approach towards test suite reduction. Information Sciences, 152(SUPPL), 89–119. https://doi.org/10.1016/S0020-0255(03)00060-4
Chung, C. G., & Lee, J. G. (1997). An enhanced zero-one optimal path set selection method. Journal of Systems and Software, 39(2), 145–164. https://doi.org/10.1016/S0164-1212(96)00169-0
Do, H., Elbaum, S., & Rothermel, G. (2005). Supporting controlled experimentation with testing techniques: An infrastructure and its potential impact. Empirical Software Engineering, 10(4), 405–435. https://doi.org/10.1007/s10664-005-3861-2
Drake, J. H., Turner, A. J., White, D. R., & Drake, J. H. (2016). Multi-objective Regression Test Suite Minimisation for Mockito. 1–6. https://doi.org/10.1007/978-3-319-47106-8
Faramarzi, A., Heidarinejad, M., Stephens, B., & Mirjalili, S. (2020). Equilibrium optimizer: A novel optimization algorithm. Knowledge-Based Systems, 191. https://doi.org/10.1016/j.knosys.2019.105190
Gupta, A., Mishra, N., & Kushwaha, D. S. (2014). Rule based test case reduction technique using decision table. Souvenir of the 2014 IEEE International Advance Computing Conference, IACC 2014, 1398–1405. https://doi.org/10.1109/IAdCC.2014.6779531
Gupta, N., Sharma, A., & Pachariya, M. K. (2020). Multi-objective test suite optimization for detection and localization of software faults. Journal of King Saud University - Computer and Information Sciences, xxxx. https://doi.org/10.1016/j.jksuci.2020.01.009
Haider, A A, Rafiq, S., & Nadeem, A. (2012). Test suite optimization using fuzzy logic. Proceedings - 2012 International Conference on Emerging Technologies, ICET 2012, September, 340–345. https://doi.org/10.1109/ICET.2012.6375440
Haider, Aftab Ali, Nadeem, A., & Rafiq, S. (2013). Computational intelligence and safe reduction of test suite. ICET 2013 - 2013 IEEE 9th International Conference on Emerging Technologies, 1–6. https://doi.org/10.1109/ICET.2013.6743502
Harris, P., & Raju, N. (2015). A greedy approach for coverage-based test suite reduction. International Arab Journal of Information Technology, 12(1), 17–23.
Harrold, M., Gupta, R., & Jean, M. (1993). A Methodology for Controlling the Size of a Test Suite. 3, 270–285. https://doi.org/10.1145/152388.152391
Jeffrey, D., & Gupta, N. (2007). by Selectively Retaining Test Cases during Test Suite Reduction. IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 33(2), 108–123.
Jeffrey, D., & Gupta, N. (2005). Test Suite Reduction with Selective Redundancy. IEEE International Conference on Software Maintenance, 549–558. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.90.1071&rep=rep1&type=pdf
Kaur, Amandeep. (2020). An Approach To Extract Optimal Test Cases Using AI. 649–654. https://doi.org/10.1109/confluence47617.2020.9058244
Kaur, Arvinder. (2017). A Comparative Study of Bat and Cuckoo Search Algorithm for Regression Test Case Selection. 2017 7th International Conference on Cloud Computing, Data Science& Engineering – Confluenc.
Kumar, N., Mishra, B., & Bali, V. (2018). A Novel Approach for Blast-Induced Fly Rock Prediction Based on Particle Swarm Optimization and Artificial Neural Network. In Lecture Notes in Networks and Systems (Vol. 34). Springer Singapore. https://doi.org/10.1007/978-981-10-8198-9_3
Lee, J. G., & Chung, C. G. (2000). An optimal representative set selection method. Information and Software Technology, 42(1), 17–25. https://doi.org/10.1016/S0950-5849(99)00052-X
Littlewood, B., & Sofer, A. (1987). A Bayesian modification to the Jelinski-Moranda software reliability growth model. Software Engineering Journal, 2(2), 30–41. https://doi.org/10.1049/sej:19870005
Loiola, C., & Maia, B. (2009). a Multi-Objective Approach for the Regression Test Case Selection Problem. XLI Brazilian Symposium of Operational Research, XLI SBPO 2009., 1824–1835.
Sprenkle, S., Sampath, S., Gibson, E., Pollock, L., & Souter, A. (2005). An empirical comparison of test suite reduction techniques for user-session-based testing of web applications. IEEE International Conference on Software Maintenance, ICSM, 2005, 587–600. https://doi.org/10.1109/ICSM.2005.18
Suri, B., Mangal, I., & Srivastava, V. (2011). Regression Test Suite Reduction using an Hybrid Technique Based on BCO And Genetic Algorithm. Special Issue of International Journal of Computer Science & Informatics, 2, 2231–5292. https://www.researchgate.net/publication/228460782
Weiguo, Wang, L., & Zhang, Z. (2019). Artificial ecosystem-based optimization: a novel nature-inspired meta-heuristic algorithm. In Neural Computing and Applications (Vol. 0123456789). Springer London. https://doi.org/10.1007/s00521-019-04452-x
Yang, X. (n.d.). Nature-Inspired Optimization Algorithms.
Yoo, S., & Harman, M. (2007). Pareto efficient multi-objective test case selection. Proceedings of the 2007 International Symposium on Software Testing and Analysis - ISSTA ’07, 140. https://doi.org/10.1145/1273463.1273483
Yoo, S., & Harman, M. (2010). Using Hybrid Algorithm For Pareto Effcient Multi-Objective Test Suite Minimisation. Journal of Systems and Software, 83(4), 689–701. https://doi.org/http://dx.doi.org/10.1016/j.jss.2009.11.706
You, L., & Lu, Y. (2012). A genetic algorithm for the time-aware regression testing reduction problem. Proceedings - International Conference on Natural Computation, Icnc, 596–599. https://doi.org/10.1109/ICNC.2012.6234754
Zhang, L., Marinov, D., Zhang, L., & Khurshid, S. (2011). An empirical study of JUnit test-suite reduction. Proceedings - International Symposium on Software Reliability Engineering, ISSRE, 4, 170–179. https://doi.org/10.1109/ISSRE.2011.26