Automated Defects Detection and Fix in Logging Statement

Renyi Zhong, Yichen Li, Jinxi Kuang, Wenwei Gu, Yintong Huo, Michael R. Lyu

Abstract: Developers use logging statements to track software runtime behaviors and system status. Yet, unclear or misleading logs can hide true execution patterns and hinder software maintenance. Current research on logging statement issues is limited, often only spotting one defect type and relying on manual corrections instead of automation. To bridge this gap, we conduct a study to identify four logging statement defect types by analyzing log-centric changes. Then we introduce LogUpdater, a two-stage framework for automatically detecting and updating these log defects. In the offline phase, LogUpdater builds a classifier using synthetic defective logs to spot defect types. During online testing, this classifier assesses if and how logs in code snippets need improvement. LogUpdater then uses type-aware prompts from past logging updates to suggest fixes via a recommendation framework based on LLMs. Results show strong defect detection with an F1 score of 0.625. It also greatly improves static text and dynamic variable suggestions by 48.12% and 24.90%, respectively. LogUpdater successfully recommends updates 61.49% of the time on new projects. We reported 40 problematic logs and their fixes on GitHub, leading to 25 merged changes across 11 projects.