Automated Deep Learning based on syntactic context Features for Log Level Recommendation

Abstract

Automated log level recommendation is a growing area of research in the field of logging. Logs are essential in software maintenance. Log levels influence the severity of the logs being printed. Recent studies have investigated different metrics for automated log level recommendation. Recently, a paper was published using automated deep learning based on syntactic context features for log level recommendation. The paper shows promising results, both for within-system evaluations and cross-system evaluations. Here, the results posed by that paper are validated by reconstructing the model from the paper. Furthermore, the model performance is evaluated on different features, for
instance, the containing block type. This study demonstrates that automated deep learning based on syntactic context features for log level recommendation certainly provides promising results. The outcomes even indicate that cross-system performance resembles within-system performance. However, this paper also indicates that the model cannot predict log levels for unseen systems. In conclusion, this paper validates that the current methodologies show potential for future research, but that the model is not ready for production. More research is necessary to transform the current algorithm into a production ready version of the algorithm.