Improving Software Reliability Through Automated Testing Frameworks in Enterprise Systems
DOI:
https://doi.org/10.15662/IJEETR.2025.0706038Keywords:
Software reliability, automated testing, unit testing, functional testing, test automation frameworks, continuous integration, enterprise systems, test runners, test design, cross-browser testing, Jasmine, Karma, regression testing, quality metricAbstract
Enterprise systems are at the heart of daily operations, and this active presence makes reliability a necessity for optimal efficiency. The paper examines the use of an automated testing structure to increase reliability by transforming checks into routines that can be repeated with the same rules and results that can be reviewed and acted upon in a timely manner. It describes how automation can be integrated with the current delivery, starting with a commit by the developer and continuing to build and test cycles and release readiness, with the feedback loops being very short to identify a mistake before a developer forgets about it. The discussion describes unit, functional, and regression testing in simple terms before demonstrating how the layers can be put together to secure logic, workflow, and long-term support across updates. It also describes the way tool decisions are determined, using the traditional combination of a test-writing framework and a browser-based runner as a real-world example of how the front-end automation is implemented. The case scenario presented in the paper explains how things are going to be different when automation becomes the norm. The concluding sections summarize the important quality steps based on which teams check the progress and the test suite health when systems increase in size.References
1. Abdulwareth, A. J., & Al-Shargabi, A. A. (2021). Toward a multi-criteria framework for selecting software testing tools. IEEE Access, 9, 158872-158891. doi: 10.1109/ACCESS.2021.3128071.
2. Afrihyia, E., Umana, A. U., Appoh, M., Frempong, D., Akinboboye, O., Okoli, I., ... & Omolayo, O. (2022). Enhancing software reliability through automated testing strategies and frameworks in cross-platform digital application environments. Journal of Frontiers in Multidisciplinary Research, 3(2), 517-531. https://doi.org/10.54660/.JFMR.2022.3.1.517-531
3. Alaskari, O., Pinedo-Cuenca, R., & Ahmad, M. M. (2021). Framework for implementation of enterprise resource planning (ERP) systems in small and medium enterprises (SMEs): A case study. Procedia Manufacturing, 55, 424-430. https://doi.org/10.1016/j.promfg.2021.10.058
4. Antonenko, A. V., Vostrikov, S. O., Burachynskyi, A. Y., Tverdokhlib, A. O., Balvak, A. A., & Slobodian, O. A. (2024). Features of automated testing using frameworks. Таврійський науковий вісник. Серія: Технічні науки, (4), 3-14. https://dspace.ksaeu.kherson.ua/bitstream/handle/123456789/10507/%D0%A2%D0%9D%D0%92_%D0%A2%D0%9D_4_2024.pdf?sequence=1&isAllowed=y#page=3
5. Bari, M. S., Sarkar, A., & Islam, S. M. (2024). AI-augmented self-healing automation frameworks: Revolutionizing QA testing with adaptive and resilient automation. AIJMR-Advanced International Journal of Multidisciplinary Research, 2(6). https://doi.org/10.62127/aijmr.2024.v02i06.1118
6. Berihun, N. G., Dongmo, C., & Van der Poll, J. A. (2023). The applicability of automated testing frameworks for mobile application testing: A systematic literature review. Computers, 12(5), 97. https://doi.org/10.3390/computers12050097
7. Blair, R. (2021). Enterprise systems and threats. https://www.iiisci.org/JOURNAL/PDV/sci/pdfs/ZA435QP21.pdf
8. Bouzenia, I., & Pradel, M. (2025). You name it, i run it: An llm agent to execute tests of arbitrary projects. Proceedings of the ACM on Software Engineering, 2(ISSTA), 1054-1076. https://doi.org/10.5281/zenodo.15202434
9. Cerny, T., Svacina, J., Das, D., Bushong, V., Bures, M., Tisnovsky, P., ... & Huang, J. (2020). On code analysis opportunities and challenges for enterprise systems and microservices. IEEE access, 8, 159449-159470. https://ieeexplore.ieee.org/abstract/document/9179733
10. Chen, T. Y., Cheung, S. C., & Yiu, S. M. (2020). Metamorphic testing: a new approach for generating next test cases. arXiv preprint arXiv:2002.12543.
11. Dalal, A. (2024). Implementing Robust Cybersecurity Strategies for Safeguarding Critical Infrastructure and Enterprise Networks. International Journal of Management, Technology And Engineering. https://www.academia.edu/download/124416737/PK_46_Implementing_Robust_Cybersecurity_Strategies_for_Safeguarding_Critical_Infrastructure_and_Enterprise_Networks.pdf
12. Dash, S. (2025). Green AI: Enhancing sustainability and energy efficiency in AI-integrated enterprise systems. IEEE Access, 13, 21216-21228. https://ieeexplore.ieee.org/abstract/document/10849555
13. Datla, L. S., & Thodupunuri, R. K. (2021). Applying formal software engineering methods to improve java-based web application quality. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 2(4), 18-26. https://doi.org/10.63282/3050-9262.IJAIDSML-V2I4P103
14. Demircioğlu, E. D., & Kalipsiz, O. (2022). API message-driven regression testing framework. Electronics, 11(17), 2671. https://doi.org/10.3390/electronics11172671
15. Drofa, D. (2025). Optimization of software development processes through the use of full-stack technologies and automation. Contemporary Issues in Artificial Intelligence, 1. https://doi.org/10.69635/ciai.2025.12
16. Fatima, S., Mansoor, B., Ovais, L., Sadruddin, S. A., & Hashmi, S. A. (2022). Automated testing with machine learning frameworks: A critical analysis. Engineering Proceedings, 20(1), 12. https://doi.org/10.3390/engproc2022020012
17. Fulcini, T., Coppola, R., Ardito, L., & Torchiano, M. (2023). A review on tools, mechanics, benefits, and challenges of gamified software testing. ACM Computing Surveys, 55(14s), 1-37. https://dl.acm.org/doi/full/10.1145/3582273
18. Garousi, V., Joy, N., Jafarov, Z., Keleş, A. B., Değirmenci, S., Özdemir, E., & Zarringhalami, R. (2024). AI-powered software testing tools: A systematic review and empirical assessment of their features and limitations. arXiv preprint arXiv:2409.00411. https://doi.org/10.48550/arXiv.2409.00411
19. Graham, O., & Paulson, M. (2025). How Artificial Intelligence Is Transforming Test Case Design and Test Data Generation in Software Testing. https://www.preprints.org/frontend/manuscript/4a901a1461108c964c94ed4e82c008cb/download_pub
20. Gudi, S. R. (2023). Enhancing reliability in java enterprise systems through comparative analysis of automated testing frameworks. International Journal of Emerging Trends in Computer Science and Information Technology, 4(2), 151-160. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I2P115
21. Gurcan, F., Dalveren, G. G. M., Cagiltay, N. E., Roman, D., & Soylu, A. (2022). Evolution of software testing strategies and trends: Semantic content analysis of software research corpus of the last 40 years. IEEE Access, 10, 106093-106109. https://ieeexplore.ieee.org/abstract/document/9910177
22. Hasan, R. (2025). A Systematic Review Of Human-AI Collaboration In It Support Services: Enhancing User Experience And Workflow Automation. American Journal of Interdisciplinary Studies, 6(3), 01-37. https://doi.org/10.63125/0fd1yb74
23. Hasselbring, W., & Van Hoorn, A. (2020). Kieker: A monitoring framework for software engineering research. Software Impacts, 5, 100019. https://doi.org/10.1016/j.simpa.2020.100019
24. Izzat, S., & Saleem, N. N. (2023). Software testing techniques and tools: A review. Journal of Education and Science, 32(2), 31-40. 10.33899/edusj.2023.137480.1305
25. Jonson, M., & Törnqvist, S. (2025). Analyzing Root Causes and Smells of Test Flakiness by Simulating Resource Usage: A study about how system resource limitations can induce flaky behavior. https://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1955392&dswid=-9573
26. Judijanto, L., Hindarto, D., Wahjono, S. I., & Djunarto, A. (2023). Edge of enterprise architecture in addressing cyber security threats and business risks. International Journal Software Engineering and Computer Science (IJSECS), 3(3), 386-396. https://pdfs.semanticscholar.org/0f2c/c27de0917d4159bbf6ceaa1681309bb79b13.pdf
27. Jyoti, S. N., Islam, M. R., & Kudapa, S. P. (2024). The Role of Test Automation Frameworks In Enhancing Software Reliability: A Review Of Selenium, Python, And API Testing Tools. International Journal of Business and Economics Insights, 4(4), 01-34. https://doi.org/10.63125/bvv8r252
28. Khankhoje, R. (2023). Revealing the foundations: The strategic influence of test design in automation. International Journal of Computer Science & Information Technology (IJCSIT) Vol, 15. https://ssrn.com/abstract=4687814
29. Klementowski, C., Reid, T., & Arnold, R. (2020). Tactical applications JavaScript development tools recommendations. https://apps.dtic.mil/sti/html/trecms/AD1090464/
30. Kohvakka, S. (2020). Automation tools in software development and production. https://lutpub.lut.fi/bitstream/handle/10024/161628/Bachelors_thesis_Sami_Kohvakka.pdf?sequence=1
31. Kothamali, P. R. (2025). Ai-powered quality assurance: Revolutionizing automation frameworks for cloud applications. Journal of Advanced Computing Systems, 5(3), 1-25. https://doi.org/10.69987/JACS.2025.50301
32. Lal, C., & Marijan, D. (2021). Blockchain testing: Challenges, techniques, and research directions. arXiv preprint arXiv:2103.10074. https://doi.org/10.48550/arXiv.2103.10074
33. Marabesi, M., García-Holgado, A., & García-Peñalvo, F. J. (2024). Exploring the connection between the TDD practice and test smells—A systematic literature review. Computers, 13(3), 79. https://doi.org/10.3390/computers13030079
34. Marijan, D., & Gotlieb, A. (2020, April). Software testing for machine learning. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 09, pp. 13576-13582). https://doi.org/10.1609/aaai.v34i09.7084
35. Nawagamuwa, J. (2023). Infrastructure as code frameworks evaluation for serverless applications testing in AWS. Tampere University. https://trepo.tuni.fi/bitstream/handle/10024/149140/NawagamuwaJanaka.pdf?sequence=2
36. Ndaba, Z., Pogiso, K., Thango, B., & Mankge, F. (2024). A systematic review of success factors and failure reasons in enterprise systems for executive, managerial, and operational support. Managerial, and Operational Support (October 19, 2024). https://dx.doi.org/10.2139/ssrn.4996122
37. Neelapu, M. (2023). Enhancement of software reliability using automatic API testing model. https://www.allmultidisciplinaryjournal.com/uploads/archives/20250408184534_MGE-2025-2-236.1.pdf
38. Nittala, E. P. (2025). AI-based autonomous code generation and optimization for enhancing software reliability in computer systems. International Journal of AI, BigData, Computational and Management Studies, 6(3), 55-64. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V6I3P107
39. Rahman, M. A., & Jyoti, S. N. (2022). A systematic literature review of user-centric design in digital business systems: Enhancing accessibility, adoption, and organizational impact. Review of Applied Science and Technology, 1(04), 01-25. https://doi.org/10.63125/ndjkpm77
40. Riccio, V., Jahangirova, G., Stocco, A., Humbatova, N., Weiss, M., & Tonella, P. (2020). Testing machine learning based systems: a systematic mapping. Empirical Software Engineering, 25(6), 5193-5254. https://doi.org/10.1007/s10664-020-09881-0
41. Rusum, G. P., & Anasuri, S. (2023). Composable enterprise architecture: A new paradigm for modular software design. International Journal of Emerging Research in Engineering and Technology, 4(1), 99-111. https://doi.org/10.63282/3050-922X.IJERET-V4I1P111
42. Savolainen, T. (2024). Improving and Automating Design System Testing. https://aaltodoc.aalto.fi/items/6d33fdfd-c523-4d6e-b429-c9fe880dd9a4
43. Srinivas, N., Mandaloju, N., & Nadimpalli, S. V. (2024). Leveraging Automation in Software Quality Assurance: Enhancing Efficiency and Reducing Defects. The Metascience, 2(4), 84-95. https://yuktabpublisher.com/index.php/TMS/article/view/208
44. Srinivas, S., & Goel, L. (2025). Designing and Implementing Robust Test Automation Frameworks using Cucumber BDD and Java. arXiv preprint arXiv:2505.17168. https://doi.org/10.48550/arXiv.2505.17168
45. Talakola, S. (2022). Exploring the effectiveness of end-to-end testing frameworks in modern web development. International Journal of Emerging Research in Engineering and Technology, 3(3), 29-39. https://ijeret.org/index.php/ijeret/article/download/119/109
46. Umar, M. A. (2023). A study of software testing: categories, levels, techniques, and types. Authorea Preprints. https://doi.org/10.36227/techrxiv.12578714.v1
47. William, S., & Virginia, W. (2022). Optimizing angular applications for enterprise-scale performance and scalability. International Journal of Trend in Scientific Research and Development, 6(7), 2340-2348. https://www.ijtsrd.com/other-scientific-research-area/other/52409/optimizing-angular-applications-for-enterprisescale-performance-and-scalability/william-shakespeare
48. Xiong, W., Legrand, E., Åberg, O., & Lagerström, R. (2022). Cyber security threat modeling based on the MITRE Enterprise ATT&CK Matrix. Software and Systems Modeling, 21(1), 157-177. https://link.springer.com/article/10.1007/s10270-021-00898-7
49. Yu Chung Wang, W., Pauleen, D., & Taskin, N. (2022). Enterprise systems, emerging technologies, and the data-driven knowledge organization. Knowledge Management Research & Practice, 20(1), 1-13. https://doi.org/10.1080/14778238.2022.2039571
