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

2023/03/02 by Amila Indika, Indika, Amila, Peter Washington +3
Computer Science · #Advanced Malware Detection Techniques #FOS: Computer and information sciences #Software Engineering (cs.SE) #Software Engineering Research #Software Reliability and Analysis Research

paper · pdf · doi:10.48550/arxiv.2303.01035

openalex publication_date 2023/03/02 · openalex created_date 2023/03/05 · openalex updated_date 2026/07/28

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 SVC classifier has the highest average F1 score of 0.5474.

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