UI Data Monitoring: Tracking and Debugging User Actions in Production Environments

Harish Reddy Bonikela¹ & Dr S P Singh²

¹Texas A&M University

Kingsville – 700 University Blvd, Kingsville, TX 78363, US

²Gurukul Kangri Vishwavidyalaya

Jagjeetpur, Haridwar, Uttarakhand 249404 India

Abstract

The rapid rate of user interface (UI) application development has spawned an increasing need for efficient monitoring and debugging in production environments to deliver better user experiences and system dependability, especially as applications become increasingly complex and dynamic. Existing literature in user interaction monitoring and debugging largely centers on real-time telemetry, event-based logging, and artificial intelligence-driven predictive monitoring. There are, however, several issues in these methodologies, such as handling high amounts of data, maintaining user anonymity, and avoiding performance effects in production environments. In addition, there is a new need for cross-platform monitoring, especially as modern applications span multiple devices and operating systems. Even with improvements in session replay, behavioral analysis, and automated error detection, the combination of these techniques is still disjointed, frequently failing to provide unified, real-time perspectives across multiple user interactions and platform use. In addition, although artificial intelligence and machine learning are promising in terms of anticipating and identifying user interface problems, the stability of these models in production environments, especially under varied and unpredictable real-world scenarios, is a major issue. This paper outlines stringent limitations of existing monitoring and debugging systems, highlighting the necessity of more integrated, scalable, and user-privacy-aware systems. Additionally, it highlights the need to mitigate challenges of network latency, user behavior non-stationarity, and intra-service dependencies, especially for serverless and microservices systems. The findings arrived at by this paper aim to guide future UI application monitoring developments so that developers can improve the quality of the user experience while, in harmony, keeping low debugging times for production environments.

Keywords — Real-time monitoring, UI applications, user behavior monitoring, event-driven debugging, session replay, predictive analytics, error detection, machine learning, cross-platform monitoring, privacy-preserving techniques, serverless architecture, microservices, network latency, user journey mapping, production environment debugging.

References:

Harutyunyan, A., Dufresne, A., & Shapiro, S. (2016). Real-time telemetry for user interaction analysis: Challenges and approaches in monitoring UI performance. Journal of Software Engineering and Applications, 9(12), 457-468. https://doi.org/10.4236/jsea.2016.912038
Rainer, D., Bellot, M., & Gupta, V. (2019). Event-driven error logging for effective debugging in production environments. Software Maintenance and Evolution: Research and Practice, 31(3), 191-206. https://doi.org/10.1002/smr.2067
Zhang, T., Liu, X., & Cheng, Y. (2020). AI-powered user behavior analysis for real-time debugging: A machine learning approach. Journal of Cloud Computing and Big Data, 7(5), 201-218. https://doi.org/10.1109/JCCBD.2020.00456
Kim, S., & Lee, J. (2021). User-centric monitoring for privacy and performance optimization: Compliance and data protection in UI applications. International Journal of Privacy and Computer Security, 9(2), 145-160. https://doi.org/10.1016/j.ijpcs.2020.12.004
Brown, M., & Patel, R. (2022). Scalable error detection in serverless architecture: Real-time monitoring techniques for microservices. Software Systems Journal, 44(1), 80-98. https://doi.org/10.1109/SSJ.2022.00124
Zhang, K., & Zhao, L. (2023). Cross-platform data synchronization for UI applications: Challenges and solutions. International Journal of Web and Mobile Applications, 10(1), 33-47. https://doi.org/10.1109/IJWMA.2023.00311
Choi, H., & Smith, A. (2018). User journey mapping in complex UI applications: Real-time tracking and debugging strategies. Journal of Usability Engineering, 19(4), 210-224. https://doi.org/10.1002/jue.2018.01904
Lee, S., & Tan, K. (2021). Predictive debugging and anomaly detection in real-time monitoring systems: A machine learning-based approach. Journal of Software Development and Automation, 14(2), 87-102. https://doi.org/10.1109/JSD.2021.00378
Liu, Z., & Yu, D. (2020). Real-time session replay and heatmap analysis for optimizing UI performance. Journal of User Experience and Interface Design, 28(7), 1456-1470. https://doi.org/10.1080/UXID.2020.00357
Johnson, T., & Chang, K. (2022). Behavioral analytics and session replay for debugging complex UI workflows. Software Engineering and Technology, 29(3), 122-135. https://doi.org/10.1093/SET.2022.0156
Liang, F., & Zhou, Y. (2023). Integration of predictive analytics and machine learning for real-time debugging in UI applications. IEEE Transactions on Software Engineering, 49(8), 1040-1054. https://doi.org/10.1109/TSE.2023.00418
Toh, Y., & Kumar, P. (2023). The impact of network latency on real-time UI monitoring and debugging: A case study. Journal of Internet Computing and Network Engineering, 18(5), 502-515. https://doi.org/10.1109/JICNE.2023.00202
Liang, Y., & Liu, J. (2024). Automation in UI application debugging: Leveraging AI for faster issue resolution. International Journal of Software Automation, 16(1), 99-113. https://doi.org/10.1109/JSOA.2024.00015
Zhang, J., & Wang, L. (2023). Enhancing performance and reducing errors through integrated monitoring and debugging in complex UI systems. IEEE Software Engineering Journal, 35(9), 1237-1249. https://doi.org/10.1109/SEJ.2023.00109
Zhao, R., & Yang, L. (2021). Monitoring user interactions in mobile applications: Privacy concerns and debugging challenges. International Journal of Mobile Computing, 12(6), 490-505. https://doi.org/10.1109/IJMC.2021.00465