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Developers write logging statements to generate logs that provide run-timeinformation for various tasks. The readability of log messages in the loggingstatements (i.e., the descriptive text) is rather crucial to the value of thegenerated logs. Immature log messages may slow down or even obstruct theprocess of log analysis. Despite the importance of log messages, there is stilla lack of standards on what constitutes good readability in log messages andhow to write them. In this paper, we conduct a series of interviews with 17industrial practitioners to investigate their expectations on the readabilityof log messages. Through the interviews, we derive three aspects related to thereadability of log messages, including Structure, Information, and Wording,along with several specific practices to improve each aspect. We validate ourfindings through a series of online questionnaire surveys and receive positivefeedback from the participants. We then manually investigate the readability oflog messages in large-scale open source systems and find that a large portion(38.1%) of the log messages have inadequate readability. Motivated by suchobservation, we further explore the potential of automatically classifying thereadability of log messages using deep learning and machine learning models. Wefind that both deep learning and machine learning models can effectivelyclassify the readability of log messages with a balanced accuracy above 80.0%on average. Our study provides comprehensive guidelines for composing logmessages to further improve practitioners' logging practices.

Are They All Good? Studying Practitioners' Expectations on the Readability of Log Messages