Enhancing Log Sentiments: An Exploratory Study of Sentiments and Emotions with Software Logs

Software logs serve as valuable resources for understanding system running and are extensively used in diverse software maintenance tasks. Logs are generated by logging statements in the code, which are written by developers. Therefore, logs may reflect developers’ sentiments about the described situations. Consequently, when developers and system administrators read logs, the sentiments embedded in logs may influence their understanding. Although the sentiments associated with logs can convey valuable information, such information is not leveraged in research and practice. Previous research has primarily relied on verbosity levels of logs to gauge sentiments, which does not really capture the sentiments and emotions perceived by humans. To bridge this gap, in this article, we first conduct an exploratory study to investigate sentiments and emotions that are communicated within logs. Our study encompasses five anomaly log datasets from LogHub and a dataset involving eight open-source Apache Java projects. We find that 8% of the logs express sentiments and emotions though developers are suggested to write them in an objective way. While most log messages might not explicitly express sentiments and emotions, they can still implicitly evoke sentiments and emotions in those who read them. Therefore, we exploit issue reports referencing logs to capture such sentiments and emotions. In these issue reports, 47.5% exhibit emotions, with 54.7% of those emotions being related to logs and 8.1% directly addressing logs. Furthermore, we demonstrate the potential of leveraging sentiment analysis to complement verbosity levels in logs, showcasing how sentiment information can offer novel insights and enhance log analysis. Specifically, by applying automatic tools, we identify 41 issue reports (9.8% on average) with negative sentiment and 55 reports (13.2% on average) with negative emotions, all referencing INFO or DEBUG logs (i.e., low severity). After manually verifying and filtering exception logs, we uncover three main concerns from 22 critical instances.