Automated Regression Testing Procedure Based on ISO/IEC 29119 to Improve Software Testing Efficiency in a Software Development Environment
DOI:
https://doi.org/10.35870/ijsecs.v6i1.6810Keywords:
Regression Testing, Test Automation, ISO/IEC 29119, Software Testing Process, Software Testing EfficiencyAbstract
The growing complexity of modern software systems has increased the importance of effective software testing practices to ensure system reliability and quality throughout the development lifecycle. Among various testing activities, regression testing plays a crucial role in verifying that newly introduced changes do not negatively affect previously functioning system components (Yoo & Harman, 2012). However, in many organizations regression testing is still conducted manually, which can be time-consuming and may delay software release cycles. This study aims to design an automated regression testing procedure based on the ISO/IEC 29119 software testing standard in order to improve testing efficiency within a software development environment. A case study was conducted at PT. ABCD to examine the existing regression testing practices. Data were collected through semi-structured interviews with members of the Quality Assurance (QA) team, direct observation of testing activities, and analysis of testing documentation. The research process consisted of analyzing the current regression testing workflow, performing gap analysis with the ISO/IEC 29119 framework, and designing an automated regression testing procedure aligned with the standard. The study focused on five system modules comprising 420 regression test cases. The results indicate that manual regression testing required approximately eight working days to complete, with an average execution time of about nine minutes per test case. After implementing the proposed automated testing procedure, the execution time was reduced to approximately one day, or about 1.14 minutes per test case, resulting in an efficiency improvement of approximately 87.5%. These findings suggest that integrating automated regression testing with a structured testing framework such as ISO/IEC 29119 can significantly improve testing efficiency while also supporting better documentation, traceability, and process consistency. From a practical perspective, the proposed approach may help development teams accelerate testing cycles and support faster delivery of software updates in dynamic development environments.
Downloads
References
Alshammari, M., & Harman, M. (2023). Data-driven approaches for regression testing optimization: A systematic mapping study. Journal of Systems and Software, 198, 111569. https://doi.org/10.1016/j.jss.2022.111569
Ammann, P., & Offutt, J. (2017). Introduction to software testing. Cambridge University Press. https://doi.org/10.1017/9781107172012
Côrtes, C. T., Oliveira, S. M. J. V. D., Santos, R. C. S. D., Francisco, A. A., Riesco, M. L. G., & Shimoda, G. T. (2018). Implementation of evidence-based practices in normal delivery care. Revista Latino-Americana de Enfermagem, 26, e2988. https://doi.org/10.1590/1518-8345.2177.2988
Fewster, M., & Graham, D. (1999). Software test automation. Addison-Wesley.
Gami, S., Katru, C., & Shah, K. (2025). Enhancing software reliability through automated CI/CD pipelines. International Journal of Computer Applications, 187(1). https://doi.org/10.5120/ijca2025924785
Graham, D. (1999). Software test automation: Effective use of test execution tools. Addison-Wesley.
Gundecha, U. (2015). Selenium testing tools cookbook. Packt Publishing.
Hasnain, M., Pasha, M. F., Ghani, I., & Jeong, S. R. (2021). Functional requirement-based test case prioritization in regression testing: A systematic literature review. SN Computer Science, 2(6), 421. https://doi.org/10.1007/s42979-021-00821-3
Humble, J., & Farley, D. (2010). Continuous delivery: Reliable software releases through build, test, and deployment automation. Pearson Education.
ISO/IEC/IEEE. (2013). ISO/IEC/IEEE 29119-1: Software and systems engineering — Software testing — Part 1: Concepts and definitions. International Organization for Standardization.
ISO/IEC/IEEE. (2013). ISO/IEC/IEEE 29119-2: Software and systems engineering — Software testing — Part 2: Test processes. International Organization for Standardization.
Jain, V. (2023). Continuous testing in CI/CD pipelines. International Journal of Innovative Research and Creative Technology, 9(1), 1–7. https://doi.org/10.5281/zenodo.14883221
Kamiya, T., Kusumoto, S., & Inoue, K. (2002). CCFinder: A multilinguistic token-based code clone detection system for large scale source code. IEEE Transactions on Software Engineering, 28(7), 654–670. https://doi.org/10.1109/TSE.2002.1019480
Lima, J. A. P., & Vergilio, S. R. (2020). Test case prioritization in continuous integration environments: A systematic mapping study. Information and Software Technology, 121, 106268. https://doi.org/10.1016/j.infsof.2020.106268
Myers, G. J. (2006). The art of software testing. John Wiley & Sons.
Pan, R., Bagherzadeh, M., Ghaleb, T. A., & Briand, L. (2022). Test case selection and prioritization using machine learning: A systematic literature review. Empirical Software Engineering, 27(2), 29. https://doi.org/10.1007/s10664-021-10054-7
Panichella, A., Kifetew, F. M., & Tonella, P. (2017). Automated test case generation as a many-objective optimisation problem with dynamic selection of the targets. IEEE Transactions on Software Engineering, 44(2), 122–158. https://doi.org/10.1109/TSE.2017.2663435
Parekh, K., & Saxena, S. (2025). Automating regression testing: Optimizing efficiency and coverage in complex software systems. International Journal for Research Publication and Seminar, 16(2), 135–142. https://doi.org/10.36676/jrps.v16.i2.58
Shahin, M., Babar, M. A., & Zhu, L. (2017). Continuous integration, delivery and deployment: A systematic review on approaches, tools, challenges and practices. IEEE Access, 5, 3909–3943. https://doi.org/10.1109/ACCESS.2017.2685629
Yoo, S., & Harman, M. (2012). Regression testing minimization, selection and prioritization: A survey. Software Testing, Verification and Reliability, 22(2), 67–120. https://doi.org/10.1002/stvr.430
Zhang, J., Liu, Y., Gligoric, M., Legunsen, O., & Shi, A. (2022, May). Comparing and combining analysis-based and learning-based regression test selection. In Proceedings of the 3rd ACM/IEEE International Conference on Automation of Software Test (pp. 17–28). https://doi.org/10.1145/3524481.3527230
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Galuh Oka Safitri, Leni Susanti
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Copyright Retention and Open Access License
Authors retain copyright of their work and grant the journal non-exclusive right of first publication under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license allows unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2. Rights Granted Under CC BY 4.0
Under this license, readers are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, including commercial use
- No additional restrictions — the licensor cannot revoke these freedoms as long as license terms are followed
3. Attribution Requirements
All uses must include:
- Proper citation of the original work
- Link to the Creative Commons license
- Indication if changes were made to the original work
- No suggestion that the licensor endorses the user or their use
4. Additional Distribution Rights
Authors may:
- Deposit the published version in institutional repositories
- Share through academic social networks
- Include in books, monographs, or other publications
- Post on personal or institutional websites
Requirement: All additional distributions must maintain the CC BY 4.0 license and proper attribution.
5. Self-Archiving and Pre-Print Sharing
Authors are encouraged to:
- Share pre-prints and post-prints online
- Deposit in subject-specific repositories (e.g., arXiv, bioRxiv)
- Engage in scholarly communication throughout the publication process
6. Open Access Commitment
This journal provides immediate open access to all content, supporting the global exchange of knowledge without financial, legal, or technical barriers.
