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.

翻译：本文提出了一种基于自然语言处理（NLP）方法启发的新型工具，用于分析.NET CLR事件日志。本研究通过详细的事件日志分析，应对了对软件系统进行有效监控和优化的日益增长的需求。我们采用了一种基于BERT的架构，并针对事件日志定制了增强的分词过程。该工具使用Python及其库以及SQLite数据库开发，既可用于学术目的的实验，也能高效解决行业新兴任务。我们的实验证明了该方法在压缩事件序列、检测重复模式和识别异常方面的有效性。训练后的模型显示出有希望的结果，在异常检测方面具有高准确率，这证明了NLP方法在提高软件系统可靠性和稳定性方面的潜力。