Patrick Loic Foalem

Patrick Loic Foalem, Andre Nguimbous, Foutse Khomh et al.

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.

Patrick Loic Foalem, Leuson Da Silva, Foutse Khomh et al.

Logging plays a central role in ensuring reproducibility, observability, and reliability in machine learning (ML) systems. While logging is generally considered a good engineering practice, poorly designed logging can negatively affect experiment tracking, security, debugging, and system performance. In this paper, we present an empirical study of logging smells in ML projects and propose a taxonomy of ML-specific logging smell types. We conducted a large-scale analysis of 444 ML repositories and manually labeled 2,448 instances of logging smells. Based on this analysis, we identified 12 categories of logging smells spanning security, metric management, configuration, verbosity, and context-related issues. Our results show that logging smells are widespread in ML systems and vary in frequency and manifestation across projects. To assess practical relevance, we conducted a survey with 27 ML practitioners. Most respondents agreed with the identified smells and reported that several types, including Logging Sensitive Data, Metric Overwrite, Missing Hyperparameter Logging, and Log Without Context, have a strong impact on reproducibility, maintainability, and trustworthiness. Other smells, such as Heavy Data Logging and Print-based Logging, were perceived as more context-dependent. We publicly release our labeled dataset to support future research. Our findings highlight logging quality as a critical and underexplored aspect of ML system engineering and open opportunities for automated detection and repair of logging issues.