NLP-Based .NET CLR Event Logs Analyzer

TL;DR

This paper introduces a novel NLP-inspired tool using BERT architecture to analyze .NET CLR event logs, improving anomaly detection and pattern recognition for software system monitoring.

Contribution

It presents a new method applying NLP techniques, specifically BERT, to event log analysis, enhancing detection of anomalies and recurring patterns in software logs.

Findings

High accuracy in anomaly detection

Effective compression of event sequences

Successful identification of recurring patterns

Abstract

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.