Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

4-2026

Abstract

Software logging is critical for system observability, yet developers face a dual crisis of costly overlogging and risky underlogging. Existing automated logging tools often overlook the fundamental whether-to-log decision and struggle with the composite nature of logging. In this paper, we propose AutoLogger, a novel hybrid framework that addresses the complete the end-to-end logging pipeline. AutoLogger first employs a fine-tuned classifier, the Judger, to accurately determine if a method requires new logging statements. If logging is needed, a multi-agent system is activated. The system includes specialized agents: a Locator dedicated to determining where to log, and a Generator focused on what to log. These agents work together, utilizing our designed program analysis and retrieval tools. We evaluate AutoLogger on a large corpus from three mature open-source projects against state-of-the-art baselines. Our results show that AutoLogger achieves 96.63% F1-score on the crucial whether-to-log decision. In an end-to-end setting, AutoLogger improves the overall quality of generated logging statements by 16.13% over the strongest baseline, as measured by an LLM-as-a-judge score. We also demonstrate that our framework is generalizable, consistently boosting the performance of various backbone LLMs.

Keywords

Automated Logging, Large Language Model, Logging Statement, Software Logging

Discipline

Artificial Intelligence and Robotics | Software Engineering

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

ICPC '26: Proceedings of the 2026 34th IEEE/ACM International Conference on Program Comprehension, Rio de Janeiro, Brazil, April 12-13

First Page

26

Last Page

37

ISBN

9798400724824

Identifier

10.1145/3794763.3794796

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.1145/3794763.3794796

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