Articles | Open Access | DOI: https://doi.org/10.37547/tajet/Volume07Issue02-11

Methodology for rapid error detection in web applications

Maksim Zemskov , Lead Software Engineer, Yandex, Serbia, Belgrade

Abstract

The article is aimed at researching and describing effective approaches and methods for quickly detecting and identifying errors in web applications while operating in production environments. This, in turn, is due to the fact that in modern conditions, the speed of detection and elimination of defects is important to ensure reliable operation of web applications.

The relevance of this topic is driven by the increasing transition of business processes to the online space. As more companies and human activities become dependent on software reliability, defects in commercial software products can lead to significant financial losses, reputational risks, and loss of user base. The growing complexity of web applications and their increasing role in critical business operations further emphasizes the importance of robust error detection methodologies. Therefore, timely detection and elimination of errors has become a vital business necessity.

The methodology described in this article represents a promising approach to rapid error detection in web applications, offering a systematic framework for monitoring and managing software errors in real-time. The methodology includes detailed recommendations and practices for identifying errors efficiently, even in software products handling high-volume error streams with substantial user loads.

The article will be useful for software developers, engineering managers, DevOps specialists, and researchers in the field of analysis and diagnostics of web applications. It provides a description of the techniques and tools used to improve the efficiency of working with web applications and improve their quality and security.

Keywords

Error monitoring, web applications, software reliability

References

Zhong H., Wang H., Mei H. (2020). Defining error signatures to detect real errors //IEEE Transactions on Software Engineering. 48 (2), 571-584.

Amankwa R. et al. (2020). An algorithm for rapid error

detection to identify potential vulnerabilities in juliet test cases //8th IEEE 2020 International Conference on Smart City and Informatization (iSCI). pp. 89-94.

Kosińska J. et al. (2023). Toward the observability of cloud-native applications: The overview of the state-of-the-art //IEEE Access. 11, 73036-73052.

Mukwevho M. A., Celik T. (2018). Toward a smart cloud: A review of fault-tolerance methods in cloud systems //IEEE Transactions on Services Computing. 14 (2), 589-605.

Jagemann S. et al. (2021). Automated error detection using symbolic root cause analysis based on data //Proceedings of the ACM SIGSAC 2021 Conference on Computer and Communication Security, 320-336.

Ratnayake R. M. D. S., Kumara B. T. G. S., Ekanayake E. B. (2021). Predicting the severity of error messages based on CNN//2021 From Innovation to Result (FITI). 1, 1-6.

Samal U., Kumar A. (2024). A software reliability model incorporating fault removal efficiency and it’s release policy //Computational Statistics. 39 (6), 3137-3155.

Dhaka R., Pachauri B., Jain A. (2022). Two-dimensional software reliability model with considering the uncertainty in operating environment and predictive analysis //Data Engineering for Smart Systems: Proceedings of SSIC 2021. – Springer Singapore. 57-69.

Sarika P. K. et al. (2023). Automating Microservices Test Failure Analysis using Kubernetes Cluster Logs //Proceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering. 192-195.

Camilli M., Janes A., Russo B.(2022). Automated test-based learning and verification of performance models for microservices systems //Journal of Systems and Software,187.1-10..

Herath J. D., Yang P., Yan G. (2021). Real-time evasion attacks against deep learning-based anomaly detection from distributed system logs //Proceedings of the Eleventh ACM Conference on Data and Application Security and Privacy. 29-40.

Cheung G. W. et al. (2024). Reporting reliability, convergent and discriminant validity with structural equation modeling: A review and best-practice recommendations //Asia Pacific Journal of Management. 41 (2),745-783.

Article Statistics

Copyright License

Download Citations

How to Cite

Maksim Zemskov. (2025). Methodology for rapid error detection in web applications. The American Journal of Engineering and Technology, 7(02), 82–90. https://doi.org/10.37547/tajet/Volume07Issue02-11