https://ginolzh.github.io/papers/ASE2023_Log_Message_Readability.pdf
Debugging and testing often require analyses of log files. This means that we need to read a lot of lines of information that can be useful, but at the same time it is difficult to parse it. Therefore, this paper is of interest for those who must read these files once in a while.
This paper investigates the readability of log messages in software logging. The authors conducted a comprehensive study involving interviews with industrial practitioners, manual investigation of log messages in open-source systems, online surveys, and the exploration of automatic classification of log message readability using machine learning.
Key findings and contributions of the paper include:
- Practitioners’ Expectations (RQ1): Through interviews, the authors identified three aspects related to log message readability: Structure, Information, and Wording. They also derived specific practices to improve each aspect. Survey participants acknowledged the importance of these aspects, with Information being considered the most critical.
- Readability in Open Source Systems (RQ2): A manual investigation of log messages from nine large-scale open-source systems revealed that 38.1% of log messages have inadequate readability, particularly in the aspect of Information.
- Automatic Classification (RQ3): The study explored the use of deep learning and machine learning models to automatically classify the readability of log messages. The models achieved a balanced accuracy above 80% on average, indicating their effectiveness.
The paper’s contributions are significant as it is one of the first studies to investigate log message readability through interviews with industrial practitioners. It highlights the prevalence of inadequate readability in log messages within large-scale open-source systems and demonstrates the potential of machine learning models to classify log message readability automatically.
The study provides systematic comprehension of log message readability and offers empirically-derived guidelines to improve developers’ logging practices. It also opens avenues for future research to establish standards for composing log messages.
The authors conclude that their study sheds light on the importance of log message readability and provides a foundation for future work to improve logging practices in software development.
