Abstract
The object of the research is a monitoring and analysis system for software quality based on user feedback collected from open-source projects on GitHub. The problem addressed is the lack of effective automated tools that can process large volumes of unstructured user feedback to identify quality issues, prioritize tasks, and detect negative trends in real time. Traditional quality assurance methods, while important, fail to capture the nuance of user sentiment and the contextual details present in natural language feedback, leading to delays in problem detection and resolution. The developed system integrates three key modules: sentiment analysis for assessing user satisfaction, issue categorization for structuring feedback into actionable types, and anomaly detection for identifying sudden changes in sentiment or feedback dynamics. The results show that transformer-based models, particularly fine-tuned BERT, outperform rule-based and traditional machine learning approaches in both accuracy and robustness. This advantage is explained by their ability to capture domain-specific language, sarcasm, and contextual dependencies, enabling more precise interpretation of complex feedback. The anomaly detection component, using LSTM autoencoders and Isolation Forest, demonstrated the ability to identify critical quality regressions up to two days before official issue reporting. These results can be applied in practice for continuous software quality monitoring in agile, open-source, or user-centric development environments where timely, data-driven decision-making is essential. The approach supports real-time insight generation, helping development teams respond proactively to quality risks and improve overall user satisfaction