Enabling Log Recommendation Through Machine Learning on Source Code

Abstract

Logging is a common practice in software development that assists developers with the maintenance of software. Logging a system optimally is a challenging task, thus Li et al. have proposed a state-of-the-art log recommendation model. However, no further attempts exist to improve the model or reproduce their results using different training data. In this research, a model was developed using the methods of Li et al. to evaluate its performance when trained on a specific dataset. Some aspects of the model such as feature filtering were studied. It was concluded that the methods of Li et al. are reproducible and can produce a model that performs well with various training data. The study on feature filtering revealed that not filtering features results in an increase of all tested metrics.