Details

Autor(en) / Beteiligte

• Rani, Pooja
• Panichella, Sebastiano
• Leuenberger, Manuel
• Di Sorbo, Andrea
• Nierstrasz, Oscar

Titel

How to identify class comment types? A multi-language approach for class comment classification

Ist Teil von

• The Journal of systems and software, 2021-11, Vol.181, p.111047, Article 111047

Ort / Verlag

Elsevier Inc

Erscheinungsjahr

2021

Link zum Volltext

Quelle

Elsevier ScienceDirect Journals Complete

Beschreibungen/Notizen

• 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. •Developers embed from high-level overview to low-level details in class comments.•Similar patterns are used across languages to write high-level details.•No common patterns are observed when implementation details are discussed.•NLP features are recommended to classify unstructured class comments.•NLP + TF–IDF features with Random forest algorithm achieves the best result.