Automatically pinpointing original logging functions from log messages for network troubleshooting
Gaspard Damoiseau-Malraux, Satoru Kobayashi, Kensuke Fukuda · 2025
Modern large-scale computer networks generate massive amounts of log data due to their increasing size, usage, and complexity. At the same time, as cloud-based businesses continue to grow, the need for services and software dedicated to log analysis is more important than ever. Although very useful, log messages often lack the necessary details for efficient troubleshooting, requiring extensive human analysis of the source code. In this paper, we present a new architecture designed with performance in mind, capable of identifying links between software-generated logs and their logging function calls in the source code (referred to as "origins" of the logs). The system we propose uses static code analysis to generate exact log templates, which are used to match log messages efficiently using a combination of a prefix tree and regular expressions. Our implementation SCOLM can pinpoint the origin of log messages with excellent performance and success rate. SCOLM can parse nearly 1 million log lines per minute on a single thread, with a match rate of 90 to 100% on our datasets. It outperforms the speed of traditional regex-based approaches, reducing the speed by about 98.7% in our experiments. The applications of this system are numerous, including live troubleshooting and statistical event analysis.