NLP-Based .NET CLR Event Logs Analyzer
This addresses the need for better software system reliability and stability, but it is incremental as it applies existing NLP methods to a new domain-specific data type.
The paper tackles the problem of analyzing .NET CLR event logs for software monitoring and optimization by developing an NLP-based tool using a BERT architecture with customized tokenization, achieving high accuracy in anomaly detection.
In this paper, we present a tool for analyzing .NET CLR event logs based on a novel method inspired by Natural Language Processing (NLP) approach. Our research addresses the growing need for effective monitoring and optimization of software systems through detailed event log analysis. We utilize a BERT-based architecture with an enhanced tokenization process customized to event logs. The tool, developed using Python, its libraries, and an SQLite database, allows both conducting experiments for academic purposes and efficiently solving industry-emerging tasks. Our experiments demonstrate the efficacy of our approach in compressing event sequences, detecting recurring patterns, and identifying anomalies. The trained model shows promising results, with a high accuracy rate in anomaly detection, which demonstrates the potential of NLP methods to improve the reliability and stability of software systems.