TL;DR
This paper presents a multi-language approach using NLP and Text Analysis to classify class comment types across Python, Java, and Smalltalk, aiding software comprehension and maintenance.
Contribution
It introduces a comprehensive taxonomy of class comment types and an automated method for accurate classification across three programming languages.
Findings
High accuracy classification of class comment types achieved
Identified commonalities and differences in comment practices
Supports improved code comment quality assessment
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/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…
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