Which log level should developers choose for a new logging statement?

Abstract

Logging statements are used to record valuable runtime information about applications. Each logging statement is assigned a log level such that users can disable some verbose log messages while allowing the printing of other important ones. However, prior research finds that developers often have difficulties when determining the appropriate level for their logging statements. In this paper, we propose an approach to help developers determine the appropriate log level when they add a new logging statement. We analyze the development history of four open source projects (Hadoop, Directory Server, Hama, and Qpid), and leverage ordinal regression models to automatically suggest the most appropriate level for each newly-added logging statement. First, we find that our ordinal regression model can accurately suggest the levels of logging statements with an AUC (area under the curve; the higher the better) of 0.75 to 0.81 and a Brier score (the lower the better) of 0.44 to 0.66, which is better than randomly guessing the appropriate log level (with an AUC of 0.50 and a Brier score of 0.80 to 0.83) or naively guessing the log level based on the proportional distribution of each log level (with an AUC of 0.50 and a Brier score of 0.65 to 0.76). Second, we find that the characteristics of the containing block of a newly-added logging statement, the existing logging statements in the containing source code file, and the content of the newly-added logging statement play important roles in determining the appropriate log level for that logging statement.