AI-Based Error Detection in Web Applications Using Machine Learning Techniques

Ramneet Singh Chadha; Poonam Mishra

doi:10.36948/ijfmr.2026.v08i03.78470

AI-Based Error Detection in Web Applications Using Machine Learning Techniques

Author(s)	Mr. Ramneet Singh Chadha, Ms. Poonam Mishra
Country	India
Abstract	Nowadays, web applications have become an essential part of business operations. They help employees complete tasks more quickly and efficiently, support better communication within organizations, and make the sharing and distribution of information easier and more effective. When APIs do not function properly, businesses can face serious consequences such as financial losses and reduced customer satisfaction. The traditional monitoring methods are usually able to detect the problems after the fact and thus lack of the ability to predict the behavior of complex error patterns. This research examines the application of machine learning for the detection of problems in web applications, utilizing the API Failure Intelligence Dataset (AFID) obtained from Kaggle. The data is first cleaned and refined to remove inconsistencies and improve its quality. In addition, domain knowledge is integrated into the dataset to make the model training process more meaningful and effective. Then, the performance of three machine learning models—Logistic Regression, Random Forest, and XGBoost—is evaluated to identify the root causes of API failures. The ensemble-based models performed well, achieving around 86% accuracy. However, a detailed analysis highlighted an inability to detect minority error classes, mainly due to the class imbalance in the dataset.
Keywords	API Error Detection, Machine Learning, Web Applications, AFID Dataset, Random Forest, XGBoost, Class Imbalance, Feature Engineering.
Field	Engineering
Published In	Volume 8, Issue 3, May-June 2026
Published On	2026-05-14
DOI	https://doi.org/10.36948/ijfmr.2026.v08i03.78470

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AI-Based Error Detection in Web Applications Using Machine Learning Techniques

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