Topic Modeling-based Logging Suggestions for Java Software Systems

Abstract

Log statements help software developers and end users get informed about different valuable run-time information while log levels categorize the severity of that information. Researchers have been working extensively on log-related problems for the last two decades. As a result, a good amount of research has been conducted on logging and its practices. However, determining which topics can be logged from a system has a potential to work on. To implement our study, first, we examined the code snippets from some renowned open-source Java language-based projects. We collected the logged methods from nine applications and after preprocessing the methods and extracting our required data, we applied some renowned topic models: Latent Dirichlet Allocation (LDA), Latent Semantic Analysis (LSA), and Non-negative Matrix Factorization (NMF). In the first part of the results, we showed how the topics are related to logging to investigate the alignment between topic modeling and logging. Our dataset, enriched with meaningful words related to method functionality, is subjected to LDA analysis. Results indicate that topics with the highest sum of word probabilities are more likely to be logged. In the second section, we listed the popular topics with their associated words from different systems generated by LDA. In the last part of the results, a comprehensive result was shown by evaluating the performance of the models using coherence scores. We believe that our research will not only be useful for its result and evaluation but also be helpful for future researchers by providing a unique dataset.