THE CONCEPT OF AN INTELLIGENT INFORMATION SYSTEM FOR CONDUCTING ACCEPTANCE TESTING OF DEEP LEARNING NEURAL NETWORKS

doi:10.26565/2519-2310-2025-2-02

Yurii Halaichuk PhD student of Computer Systems and Robotics Department Institute of Computer Science and Artificial Intelligence, V. N. Karazin Kharkiv National University https://orcid.org/0009-0004-1048-9425
Maryna Miroshnyk Doctor of technical sciences, Professor of Computer Systems and Robotics Department Institute of Computer Science and Artificial Intelligence, V. N. Karazin Kharkiv National University, Ukraine https://orcid.org/0000-0002-2231-2529
Elvira Kulak PhD, Associate Professor, Associate Professor of Design Automation Department, Kharkiv National University of Radio Electronics https://orcid.org/0000-0002-8441-5187

DOI: https://doi.org/10.26565/2519-2310-2025-2-02

Keywords: deep learning, intelligent information system, quality assurance, prediction model, user acceptance testing

Abstract

In the modern world, an increasing number of critical infrastructures and commercial systems rely on the results of computations by artificial intelligence algorithms, particularly neural networks. In parallel, the process of evaluating the quality of these algorithms and ensuring proper execution of all stages of their testing has become highly significant to eliminate potential flaws and ensure their ability to deliver expected results. The article addresses the issue of improving the User Acceptance Testing (UAT) process for domain-specific software utilizing deep learning neural networks. It examines challenges related to limited resources, insufficient UAT team expertise in machine learning, and the complexity of testing systems that continue learning post-initial development. A general overview of existing solutions is provided, highlighting their advantages and drawbacks. A concept of an intelligent information system based on a predictive model for evaluating neural network quality metrics, specifically accuracy and loss function is proposed, enabling the
quality assessment process of such networks using a set of training and validation data. An experimental methodology is described, including the algorithm of development of a predictive model for analyzing network quality trends and the creation of an intelligent information system to streamline and accelerate the UAT process. The system’s component deployment architecture is presented, covering interactions between client applications, a web server, an execution server, and a database, leveraging modern network protocols and technologies. The research results aim to enhance UAT efficiency through automation and the application of a predictive model to obtain dynamic quality metrics for deep learning neural network algorithms.

Downloads

Download data is not yet available.

References

Directive (Goodfellow I., Bengio Y., Courville A. (2016) Deep Learning. Cambridge: MIT Press, 800 с. Deep Learning - Ian Goodfellow, Yoshua Bengio, Aaron Courville https://books.google.com.ua/books/about/Deep_Learning.html?id=Np9SDQAAQBAJ&redir_esc=y

Sommerville I. (2015) Software Engineering. 10th ed. Boston: Pearson, 816 с.Software Engineering,Global Edition - Ian Sommerville https://books.google.com.ua/books/about/Software_Engineering_Global_Edition.html?id=W_LjCwAAQBAJ&redir_esc=y

Myers G.J., Sandler C., Badgett T. (2011) The Art of Software Testing, 3rd ed, New York: Wiley, 256 с. The Art of Software Testing - Glenford J. Myers, Corey Sandler, Tom Badgett https://books.google.com.ua/books/about/The_Art_of_Software_Testing.html?id=GjyEFPkMCwcC&redir_esc=y

ISO/IEC/IEEE 29119-1:2013. (2013) Software and Systems Engineering. Software Testing Part 1:Concepts and Definitions. Geneva: International Organization for Standardization.ISO/IEC/IEEE29119-1:2013 | IEC https://webstore.iec.ch/en/publication/11972

International Software Testing Qualifications Board. (2023). ISTQB Certified Tester Foundation Level Syllabus (Version 4.0.1). URL: https://www.istqb.org/certifications/ certified-tester-foundation-level

Russell S., Norvig P. (2020) Artificial Intelligence: A Modern Approach,4th ed, Boston:Pearson, 1152 с. Artificial Intelligence: A Modern Approach, 4th US ed. https://aima.cs.berkeley.edu/

Tian, Y., Pei, K., Jana, S., & Ray, B. (2018). DeepTest: Automated Testing of Deep-Neural-Network-Driven Autonomous Cars. Proceedings of the 40th International Conference on Software Engineering. DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars https://arxiv.org/pdf/1708.08559v1

Lundberg S.M., Lee S.I. (2017) A Unified Approach to Interpreting Model Predictions // Advances in Neural Information Processing Systems (NeurIPS), Vol. 30, с. 4765-4774. DOI: https://doi.org/10.48550/arXiv.1705.07874

Ribeiro M.T., Singh S., Guestrin C. (2016) "Why Should I Trust You?": Explaining the Predictions of Any Classifier // Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, c. 1135-1144. DOI: https://doi.org/10.1145/2939672.2939778

He K. (2016) Deep Residual Learning for Image Recognition / K. He, X. Zhang, S. Ren, J. Sun // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). - P.770-778.https://doi.org/10.48550/arXiv.1512.03385

Zhang J. M., Harman M., Ma L., Liu Y. (2020) Machine Learning Testing: Survey, Landscapes and Horizons // IEEE Transactions on Software Engineering. Vol. 48 No. 1, с. 1-36. DOI: https://doi.org/10.1109/TSE.2019.2962027

Axel K. (1998) Using UML for Business Object Based Systems Modeling / Korthaus Axel // ResearchGate. https://doi.org/10.1007/978-3-642-48673-9_15

Nawroze I. (2023) A Comparative Analysis of PHP and Python Programming Languages for Optimal Software Development / I. Nawroze, C. Rubel // International Journal of Information Technology 8(1):1-13, URL: http://dx.doi.org/10.6084/m9.figshare.24885846.v1

Faisal Qureshi, Haida Rasheed. (2022) Comparative Analysis of Modern Database Technologies for Scalable Data Storage in AI-Driven Ecommerce Applications / Faisal Qureshi, Haida Rasheed //ResearchGate, URL: http://dx.doi.org/10.13140/RG.2.2.14668.83848

Howard A.G. (2017) MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications / A. G. Howard, M. Zhu, B. Chen [et al.]. - URL: https://arxiv.org/abs/1704.04861

Tan M. (2019) EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks / M. Tan, Q. V. Le // International Conference on Machine Learning (ICML). https://doi.org/10.48550/arXiv.1905.11946

Hochreiter S. (1997) Long Short-Term Memory / S. Hochreiter, J. Schmidhuber // Neural Computation.- Vol. 9, Iss. 8. - P. 1735-1780.https://doi.org/10.1162/neco.1997.9.8.1735

Vaswani A. (2017) Attention is All You Need / A. Vaswani, N. Shazeer, N. Parmar [et al.] // Advances in Neural Information Processing Systems (NIPS). - P. 5998-6008. https://doi.org/10.48550/arXiv.1706.03762

Ismail Fawaz H. (2019) Deep learning for time series classification: a review / H. Ismail Fawaz, G. Forestier, J. Weber [et al.] // Data Mining and Knowledge Discovery. - Vol. 33. - P. 917-963. https://link.springer.com/article/10.1007/s10618-019-00619-1

Domhan T. (2015) Speeding Up Automatic Hyperparameter Optimization of Deep Neural Networks by Extrapolating Learning Curves / T. Domhan, J. T. Springenberg, F. Hutter // Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI).- P. 3460-3468.https://dl.acm.org/doi/10.5555/2832581.2832731