An Empirical Study on Logging Evolution On Stack Overflow: Trends, Topics, and Challenges

2026-05-27

AI Summary

This study addresses the lack of systematic understanding of developers’ logging practices by analyzing over 210,000 logging-related questions from Stack Overflow. It presents the first thematic categorization of logging topics that integrates large language models with manual validation and introduces multidimensional community metrics—such as the proportion of questions without accepted answers and median time to resolution—to quantitatively assess topic difficulty and popularity. The analysis identifies eleven distinct logging themes, revealing that logging in containerized environments is the most challenging, with 64.9% of related questions lacking an accepted answer. These findings offer empirical insights and actionable guidance for practitioners, tool designers, and educators seeking to improve logging practices and support in modern software development contexts.

Abstract

Context: Logging is a crucial practice in software engineering, aiding developers in debugging applications when errors occur. While existing research has explored logging challenges from an academic perspective through literature reviews and source code analysis, a comprehensive study from the practitioners' perspective remains lacking. Objective: This paper aims to bridge this knowledge gap by presenting an in-depth analysis of trends, topics, and challenges in logging based on a dataset of 216,094 posts from Stack Overflow (SO), a popular Q\&A platform for developers. Method: We analyzed longitudinal trends by examining metadata related to users, questions, and tags associated with logging discussions. To identify prevalent discussion topics, we employed a Large Language Model (LLM)--based classification approach, based on a manually validated ground-truth sample. Topic popularity was assessed through average scores and views, while difficulty was measured using three community-driven metrics: the proportion of questions without accepted answers, the proportion of unanswered questions, and the median time to receive an accepted answer. Results: Our analysis identifies 11 distinct topics, with the top three (General Logging Practices, Error Handling and Debugging, and Logging Levels and Output) accounting for over 70\% of all logging-related discussions. Notably, Logging in Containerized Environments emerged as the most difficult topic: 64.9\% of its questions lack an accepted answer, and its median resolution time is among the highest. These findings highlight enduring practitioner struggles with logging in Docker or other containerized environments and the integration of logging pipelines into orchestrators such as Kubernetes and cloud environments. Conclusion: This study sheds light on the practical challenges of logging and provides actionable insights for developers, framework vendors, researchers, and educators.

Problem

• logging
• Stack Overflow
• containerized environments
• software engineering
• practitioner challenges

Innovation

• logging
• Stack Overflow
• Large Language Model
• containerized environments
• empirical study