arXiv:2602.07698v2 Announce Type: replace-cross Abstract: Context: Log parsing is a critical standard operating procedure in software systems, enabling monitoring, anomaly detection, and failure diagnosis. However, automated log parsing remains challenging due to heterogeneous log formats, distribution shifts between training and deployment data, and the brittleness of rule-based approaches. Objectives: This study aims to systematically evaluate how sequence modelling architecture, representation choice, sequence length, and training data availability influence automated log parsing performanc
The increasing complexity and distributed nature of software systems, coupled with advances in sequence-to-sequence models, make automated log parsing a critical area for improvement.
Improved automated log parsing can significantly enhance software reliability, security, and operational efficiency, reducing downtime and maintenance costs for critical infrastructure.
The adoption of more robust and adaptive log parsing techniques, moving beyond brittle rule-based systems, will allow for better monitoring and anomaly detection in dynamic environments.
- · Software companies
- · Cloud providers
- · DevOps teams
- · AI/ML researchers in NLP
- · Manual log analysis service providers
- · Companies with legacy monitoring systems
More efficient and reliable software operations due to improved anomaly detection and failure diagnosis.
Reduced operational expenditure for enterprises and potentially higher service availability across various digital platforms.
The development of more resilient and self-healing AI-driven software systems that can pre-emptively address issues.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL