Systematic Mapping of Fault Monitoring Techniques Based on Data Mining - Rodrigues, Paulo Ricardo Fernandes; Lelli, Valéria (Proceedings of the Brazilian Symposium on Multimedia and the Web (WebMedia), 2025)
The increasing complexity and interconnectivity of modern software systems have made fault detection a significant challenge. Traditional testing techniques often fall short, especially in dynamic and large-scale environments. To address this issue, various real-time monitoring approaches based on data mining have been proposed. These approaches, through continuous system analysis and the use of anomaly detection techniques, are capable of identifying faults in software systems. This work presents a systematic mapping of the literature on these monitoring techniques, aiming to identify implementation challenges and provide a comprehensive overview of the current state of research. The mapping was conducted using the Scopus database and 84 studies were selected to help answer the research questions defined in this work. This analysis identified the main fault detection techniques, such as neural networks and Support Vector Machines (SVMs), the types of systems monitored, including Cloud and Big Data platforms, common fault categories like cybersecurity and performance, and the evaluation metrics used, such as the F1-score and prediction time. Despite these established approaches, the study also revealed significant challenges that hinder progress, including the difficulty of labeling data for supervised models, the complexity of interpreting the generated models, and the need for computationally efficient monitoring.