Abstract:
In this paper, we present a tool for analyzing .NET CLR event logs based on a novel method inspired by Natural Language Processing approach. Our research addresses the growing demand 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.
Demo video:https://youtu.be/JLCS4F-AlYc GitHub:https://github.com/ironSensei/NLP-CLR-LogAnalyzer
Key words and phrases:.NET CLR, BERT, patterns, anomalies.
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