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https://doi.org/10.21256/zhaw-23351| Publikationstyp: | Beitrag in wissenschaftlicher Zeitschrift |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | How to identify class comment types? : a multi-language approach for class comment classification |
| Autor/-in: | Rani, Pooja Panichella, Sebastiano Leuenberger, Manuel Di Sorbo, Andrea Nierstrasz, Oscar |
| et. al: | No |
| DOI: | 10.1016/j.jss.2021.111047 10.21256/zhaw-23351 |
| Erschienen in: | Journal of Systems and Software |
| Band(Heft): | 181 |
| Heft: | 111047 |
| Erscheinungsdatum: | 19-Jul-2021 |
| Verlag / Hrsg. Institution: | Elsevier |
| ISSN: | 0164-1212 1873-1228 |
| Sprache: | Englisch |
| Schlagwörter: | Natural language processing technique; Code comment analysis; Software documentation |
| Fachgebiet (DDC): | 005: Computerprogrammierung, Programme und Daten 006: Spezielle Computerverfahren |
| Zusammenfassung: | 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 languages. We propose an approach that leverages two techniques, namely Natural Language Processing and Text Analysis, to automatically identify various types of information from class comments 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 Python, Java, and Smalltalk programming languages. We believe this work can help to monitor and assess the quality and evolution of code comments in different program languages, and thus support maintenance and evolution tasks. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/23351 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | CC BY-NC-ND 4.0: Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International |
| Departement: | School of Engineering |
| Organisationseinheit: | Institut für Informatik (InIT) |
| Publiziert im Rahmen des ZHAW-Projekts: | COSMOS – DevOps for Complex Cyber-physical Systems of Systems |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| 2021_Rani-etal_Identify-class-comment-types.pdf | 2.17 MB | Adobe PDF |  Öffnen/Anzeigen |
Zur Langanzeige
Rani, P., Panichella, S., Leuenberger, M., Di Sorbo, A., & Nierstrasz, O. (2021). How to identify class comment types? : a multi-language approach for class comment classification. Journal of Systems and Software, 181(111047). https://doi.org/10.1016/j.jss.2021.111047
Rani, P. et al. (2021) ‘How to identify class comment types? : a multi-language approach for class comment classification’, Journal of Systems and Software, 181(111047). Available at: https://doi.org/10.1016/j.jss.2021.111047.
P. Rani, S. Panichella, M. Leuenberger, A. Di Sorbo, and O. Nierstrasz, “How to identify class comment types? : a multi-language approach for class comment classification,” Journal of Systems and Software, vol. 181, no. 111047, Jul. 2021, doi: 10.1016/j.jss.2021.111047.
RANI, Pooja, Sebastiano PANICHELLA, Manuel LEUENBERGER, Andrea DI SORBO und Oscar NIERSTRASZ, 2021. How to identify class comment types? : a multi-language approach for class comment classification. Journal of Systems and Software. 19 Juli 2021. Bd. 181, Nr. 111047. DOI 10.1016/j.jss.2021.111047
Rani, Pooja, Sebastiano Panichella, Manuel Leuenberger, Andrea Di Sorbo, and Oscar Nierstrasz. 2021. “How to Identify Class Comment Types? : A Multi-Language Approach for Class Comment Classification.” Journal of Systems and Software 181 (111047). https://doi.org/10.1016/j.jss.2021.111047.
Rani, Pooja, et al. “How to Identify Class Comment Types? : A Multi-Language Approach for Class Comment Classification.” Journal of Systems and Software, vol. 181, no. 111047, July 2021, https://doi.org/10.1016/j.jss.2021.111047.
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