Authors : Nusrat Yasmin Nadia; MD Shadikul Bari; Mohammed Majid Bakhsh; Ankur Sarkar; S A Mohaiminul Islam

Volume/Issue : Volume 10 - 2025, Issue 1 - January

Google Scholar : https://tinyurl.com/32fx5ss5

Scribd : https://tinyurl.com/y98zx9vj

DOI : https://doi.org/10.5281/zenodo.14874188

Abstract : This requirement has become more so as BI applications expand in terms of functionality and volume where TDM has been deemed more important. Currently, there is a lot of hassle with regard to both the generation, management and validation of test data used in traditional testing processes, that does not adequately address the adaptive requirements of contemporary BI solutions leading to decreased efficiency, inadequate quality and incoherent outcomes as well as unrealistic data fidelity. Finally, this article aims to look at how AI technology can rise to the occasion of these Alarge-scale BI applications by analyzing the case of AI-assisted TDM. It continues with the discussion of test data generation using automated means and presents some practical AI methods as machine learning or the application of neural networks and generative models in this concern. In addition, we explore the ways in which BI systems can incorporate AI to solve a number of questions concerning large quantities of data, data selection, and the validation of the results achieved through synthetic data. Main characteristics of AI-based TDM platform such as large scale processing, integration of synthetic data, and optimized data handling are described together with case studies that illustrate benefits for software testing effectiveness and BI app quality. Moreover, the paper presents the limitations and drawbacks of the AI-based solutions, including the possibilities of data privacy and ethical issues and how to overcome them and the barriers to organizational adoption of AI and its solutions. Finally, possibilities and the general trend in the future of AI in BI testing are discussed, and the future development of creating new and more efficient applications of AI in BI testing for enhancing analytics and reporting is recommended.

Keywords : AI-Driven Test Data Management, Business Intelligence Applications, Automated Test Data Generation, Machine Learning In Software Testing, Synthetic Data, BI Testing, Large-Scale Data Management, AI In Business Analytics, Test Data

References :

1. Berge, Z. L., & Muilenburg, L. (2014). Handbook of research on educational communications and technology (4th ed.). Springer.
2. Kapur, R., & Yadav, N. (2018). Artificial intelligence in software testing: Techniques and challenges. International Journal of Engineering & Technology, 7(4.3), 183-187.
3. Kumari, S., & Verma, R. (2019). A survey on automated test data generation techniques. International Journal of Advanced Computer Science and Applications, 10(6), 119-125.
4. Sharma, A., & Sharma, R. (2017). Machine learning techniques in software testing. International Journal of Computer Applications, 158(7), 7-11.
5. McCool, M., & Despain, D. (2017). Challenges in managing large datasets for data-driven BI applications. Journal of Big Data Analytics, 2(1), 12-19.
6. Zhu, Y., & Huang, W. (2020). Data processing in large-scale systems: Leveraging big data and machine learning. International Journal of Computer Science, 29(2), 243-257.
7. Lee, K., & Jones, M. (2018). The importance of representative data in BI testing. International Journal of Business Intelligence, 5(3), 213-219.
8. García, L., & Sánchez, F. (2019). AI in test data management: Improving data preparation for business intelligence applications. Computational Intelligence in Business Systems, 8(4), 112-123.
9. Nanda, S., & Patel, A. (2020). Developing AI-based platforms for data management in BI applications. Journal of Software Engineering and Applications, 13(8), 111-124.
10. Rao, P., & Chauhan, P. (2019). Machine learning and AI for scalable data management solutions in software testing. International Journal of Computational Intelligence and Applications, 18(1), 87-100.
11. Sweeney, L., & Cummings, J. (2017). Data privacy and ethical challenges in AI-driven systems. Journal of Data Protection & Privacy, 3(2), 165-175.
12. Stoyanov, L., & Ivanov, A. (2020). Mitigating risks in AI systems: Legal and technical considerations. International Journal of Artificial Intelligence Ethics, 4(2), 45-58.
13. Mitchell, T., & Li, S. (2021). The future of AutoML and AI in test data management. International Journal of Machine Learning Research, 32(3), 51-63.
14. Ahmed, H., & Rana, R. (2021). The next phase of AI in BI testing: The role of explainable AI. Journal of AI Research and Applications, 18(5), 176-188.
15. Zhou, H., & Guo, L. (2019). AI-driven test data management in business intelligence applications: A survey. Journal of Artificial Intelligence Research, 57(6), 235-249.