Performance Comparison of Binary Machine Learning Classifiers in Identifying Code Comment Types: An Exploratory Study

Abstract

Code comments are vital to source code as they help developers with program comprehension tasks. Written in natural language (usually English), code comments convey a variety of different information, which are grouped into specific categories. In this study, we construct 19 binary machine learning classifiers for code comment categories that belong to three different programming languages. We present a comparison of performance scores for different types of machine learning classifiers and show that the Linear SV C classifier has the highest average Fl score of 0.5474.

Anthony S. Peruma

Assistant Professor

My research interests include program comprehension and software refactoring.