# Automated Defects Detection and Fix in Logging Statement

**Authors:** Renyi Zhong, Yichen Li, Jinxi Kuang, Wenwei Gu, Yintong Huo, Michael R. Lyu  
**arXiv:** [2408.03101](https://arxiv.org/abs/2408.03101)  
**Date:** 2024-08-06  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2408.03101v1/

## Key points

- Authors address the issue of misleading logs complicating software maintenance by obscuring actual activities
- Conducted a comprehensive analysis to identify four types of defects in logging statements
- Introduced LogFixer, a two-stage framework for automatic detection and updating of logging statements
- Utilized a similarity-based classifier in the offline stage to accurately identify defects in logs
- Implemented an LLM-based recommendation framework for suggesting updates based on historical log changes
- Achieved an impressive F1 score of 0.625 and significantly enhanced suggestions for static text and dynamic variables
- Successfully recommended correct updates for new projects with a success rate of 61.49%
- Reported 40 problematic logs to GitHub resulting in 25 confirmed and merged changes across 11 projects

## Summary

In the study "Automated Defects Detection and Fix in Logging Statement," authors Renyi Zhong, Yichen Li, Jinxi Kuang, Wenwei Gu, Yintong Huo, and Michael R. Lyu address the issue of misleading logs complicating software maintenance by obscuring actual activities. Existing research on logging quality problems has been limited to single defects and manual fixes. To tackle this challenge, the authors conducted a comprehensive analysis of real-world log changes to identify four types of defects in logging statements. They introduced LogFixer, a two-stage framework designed for automatic detection and updating of logging statements. In the offline stage of LogFixer, a similarity-based classifier is utilized on synthetic defective logs to accurately identify defects. During the online phase, this classifier evaluates logs within code snippets to determine necessary improvements. Additionally, an LLM-based recommendation framework suggests updates based on historical log changes. The effectiveness of LogFixer was evaluated using both real-world and synthetic datasets as well as new projects, achieving an impressive F1 score of 0.625. Notably, LogFixer significantly enhanced suggestions for static text and dynamic variables by 48.12% and 24.90%, respectively. Moreover, it achieved a commendable success rate of 61.49% in recommending correct updates for new projects. As part of their evaluation process, the authors reported 40 problematic logs to GitHub which resulted in 25 confirmed and merged changes across 11 projects. Overall, this study provides valuable insights into improving logging statement quality through automated defect detection and fixing mechanisms with the innovative LogFixer framework developed by the authors.