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How to Identify Class Comment Types? A Multi-language Approach for Class Comment Classification

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Rani Pooja, Panichella Sebastiano, Leuenberger Manuel, Di Sorbo Andrea, Nierstrasz Oscar,
Project Agile Software Assistance
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Original article (peer-reviewed)

Journal Journal of Systems and Software
Publisher Elsevier, NA
Volume (Issue) 181
Page(s) 111047 - 111047
Title of proceedings Journal of Systems and Software
DOI 10.1016/j.jss.2021.111047

Open Access

URL https://www.sciencedirect.com/science/article/pii/S0164121221001448
Type of Open Access Publisher (Gold Open Access)

Abstract

Most software maintenance and evolution tasks require developers to understand the source code of their software systems. Software developers usually inspect class comments to gain knowledge about program behavior, regardless of the programming language they are using. Unfortunately, (i) different programming languages present language-specific code commenting notations and guidelines; and (ii) the source code of software projects often lacks comments that adequately describe the class behavior, which complicates program comprehension and evolution activities. To handle these challenges, this paper investigates the different language-specific class commenting practices of three programming languages: Python, Java, and Smalltalk. In particular, we systematically analyze the similarities and differences of the information types found in class comments of projects developed in these languages. We propose an approach that leverages two techniques -namely Natural Language Processing and Text Analysis -to automatically identify class comment types, i.e., the specific types of semantic information found in class comments. To the best of our knowledge, no previous work has provided a comprehensive taxonomy of class comment types for these three programming languages with the help of a common automated approach. Our results confirm that our approach can classify frequent class comment information types with high accuracy for the Python, Java, and Smalltalk programming languages. We believe this work can help in monitoring and assessing the quality and evolution of code comments in different programming languages, and thus support maintenance and evolution tasks.
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