Hybrid Framework To Exclude Similar and Faulty Test Cases In Regression Testing
Main Article Content
Abstract
Regression testing is a crucial phase in the software development lifecycle that makes sure that new changes/updates in the software system don’t introduce defects or don’t affect adversely the existing functionalities. However, as the software systems grow in complexity, the number of test cases in regression suite can become large which results into more testing time and resource consumption. In addition, the presence of redundant and faulty test cases may affect the efficiency of the regression testing process. Therefore, this paper presents a new Hybrid Framework to Exclude Similar & Faulty Test Cases in Regression Testing (ETCPM) that utilizes automated code analysis techniques and historical test execution data to identity and exclude redundant, similar and faulty test cases from the given regression suite. Our experimental results clearly show the benefits of the ETCPM framework in terms of reduction in the testing time, optimization of the resource allocation, and improvement in the overall quality of regression test suite. ETCPM enables software development teams to achieve faster and reliable regression testing by intelligent exclusion of similar and fault test cases, which yields in reduction in the software delivery cycles and better end user satisfaction.
Received 27/09/2023
Revised 10/02/2024
Accepted 12/02/2024
Published 25/02/2024
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
References
Muhammad H., Imran G., Muhammad F. P., and Seung R. J. A comprehensive review on regression test case prioritization techniques for web services. KSII Trans. Internet Inf. Syst. 2020; Vol. 14:No.5 . https://doi.org/10.3837/tiis.2020.05.001
Khatibsyarbini, M., et al., Test case prioritization approaches in regression testing: A systematic literature review. Inf Softw Technol. 2018; 93: p. 74-93. https://doi.org/10.1016/j.infsof.2017.08.014
Z. Yuan, Y. Lou, M. Liu, S. Ding, K. Wang, Y. Chen, and X. Peng.No more manual tests? evaluating and improving chatgpt for unit test generation. arXiv preprint arXiv:2305.04207. 2023. https://doi.org/10.1145/3395363.3397383
Mukherjee R. and Patnaik K. A survey on different approaches for software test case prioritization. J. King Saud Univ. Comput. Inf. Sci. Oct.2018; S1319157818303616. https://doi.org/10.1016/j.jksuci.2018.09.005
M. I. Younis, “DEO: a dynamic event order strategy for T-way sequence covering array test data generation,” Baghdad Sci. J. 2020;vol. 17, no. 2: p. 575, May. https://doi.org/10.21123/bsj.2020.17.2.0575
Bukhsh FA, Bukhsh ZA, Daneva M. A systematic literature review on requirement prioritization techniques and their empirical evaluation. Comput. Stand. Interfaces. 2020;69:103389. https://doi.org/10.1016/j.csi.2019.103389
Younis, M.I., Alsewari, A.R.A., Khang, N.Y., Zamli, K.Z., CTJ: Input-output based relation combinatorial testing strategy using jaya algorithm. Baghdad Sci. J. 2020;17 (3): pp. 1002-1009. https://doi.org/10.21123/BSJ.2020.17.3(SUPPL.).1002
Rongqi Pan, Mojtaba Bagherzadeh, Taher A. Ghaleb, and Lionel Briand. 2022. Test case selection and prioritization using machine learning: A systematic literature review. Empir. Softw. Eng. 2022;27, 2: 1–43. https://doi.org/10.1007/s10664-021-10066-6
Ali, S., et al., Enhanced regression testing technique for agile software development and continuous integration strategies. Softw. Qual. J. .2020; Vol. 28: p 397–423. https://doi.org/10.1007/s11219-019-09463-4
Pandey, A. and S. Banerjee.Test Suite Minimization in Regression Testing Using Hybrid Approach of ACO and GA. IJAMC. 2018; 9. https://doi.org/ 10.4018/IJAMC.2018070105
Agrawal, A.P. and A. Kaur, A comprehensive comparison of ant colony and hybrid particle swarm optimization algorithms through test case selection, in Data engineering and intelligent computing. 2018, Springer. p. 397-405. https://doi.org/10.1007/978-981-10-3223-3_38
Chen et al., Chen J., Shang W., Shihab E., Perfjit: Test-level just-in-time prediction for performance regression introducing commits IEEE Trans. Softw. Eng. 2020; p. 1. https://doi.org/10.1109/TSE.2020.3023955
Singhal, S.; Jatana, N.; Subahi, A.F.; Gupta, C.; Khalaf, O.I.; Alotaibi, Y. Fault Coverage-Based Test Case Prioritization and Selection Using African Buffalo Optimization. Comput. Mater. Contin. 2023; 74: 6755–6774.https://doi.org/10.32604/cmc.2023.032308
Q. Zheng, Z. Ou, L. Liu, T. Liu, A novel method on software structure evaluation, in: Proceedings of the 2nd IEEE International Conference on Software Engineering and Service, ICSESS ’11, IEEE.2011; pp. 251–254. https://doi.org/10.1016/j.jss.2020.110539
Magalhães, C., et al., HSP: A hybrid selection and prioritisation of regression test cases based on information retrieval and code coverage applied on an industrial case study. J. Syst. Softw. 2020; 159: p. 110430. https://doi.org/10.1016/j.jss.2019.110430