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

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Computer science Artificial intelligence Natural language processing Comprehension Classifier (UML) Machine learning Source code Binary classification Code (set theory) Construct (python library) Program comprehension Programming language Support vector machine Software Software system Set (abstract data type)

Amila Indika , Peter Washington , Anthony Peruma ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2303.01035 · OA: W4323066744

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 SVC classifier has the highest average F1 score of 0.5474.