Falconf: A Web-Based Tool for Misconfiguration Diagnosis via Log Analysis

Abstract

Misconfigurations continue to be one of the major causes of failures in distributed software systems, yet diagnosing them remains challenging. This is because faulty configuration parameters are difficult to identify among hundreds of configuration entries, and their effects often leave no explicit evidence in runtime logs. We present Falconf, a web-based tool for automatically diagnosing misconfigurations in distributed software systems using log analysis. Falconf takes runtime log files as input, analyzes them using a pre-trained two-stage transformer model, and produces a ranked list of suspicious configuration parameters that are likely responsible for the observed failure. The tool features an intuitive web interface that visualizes the diagnosis workflow, allowing users to select the target application and version, input log files, and inspect the ranked results without any expertise in machine learning or log analysis. We demonstrate that Falconf can effectively diagnose misconfigured parameters from real-world logs of distributed software systems, including Apache Spark and Hadoop.