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

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

doi:10.1016/j.jss.2021.111047

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-23351

Full metadata record

DC Field	Value	Language
dc.contributor.author	Rani, Pooja	-
dc.contributor.author	Panichella, Sebastiano	-
dc.contributor.author	Leuenberger, Manuel	-
dc.contributor.author	Di Sorbo, Andrea	-
dc.contributor.author	Nierstrasz, Oscar	-
dc.date.accessioned	2021-10-30T12:19:32Z	-
dc.date.available	2021-10-30T12:19:32Z	-
dc.date.issued	2021-07-19	-
dc.identifier.issn	0164-1212	de_CH
dc.identifier.issn	1873-1228	de_CH
dc.identifier.uri	https://digitalcollection.zhaw.ch/handle/11475/23351	-
dc.description.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 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.	de_CH
dc.language.iso	en	de_CH
dc.publisher	Elsevier	de_CH
dc.relation.ispartof	Journal of Systems and Software	de_CH
dc.rights	http://creativecommons.org/licenses/by-nc-nd/4.0/	de_CH
dc.subject	Natural language processing technique	de_CH
dc.subject	Code comment analysis	de_CH
dc.subject	Software documentation	de_CH
dc.subject.ddc	005: Computerprogrammierung, Programme und Daten	de_CH
dc.subject.ddc	006: Spezielle Computerverfahren	de_CH
dc.title	How to identify class comment types? : a multi-language approach for class comment classification	de_CH
dc.type	Beitrag in wissenschaftlicher Zeitschrift	de_CH
dcterms.type	Text	de_CH
zhaw.departement	School of Engineering	de_CH
zhaw.organisationalunit	Institut für Informatik (InIT)	de_CH
dc.identifier.doi	10.1016/j.jss.2021.111047	de_CH
dc.identifier.doi	10.21256/zhaw-23351	-
zhaw.funding.eu	info:eu-repo/grantAgreement/EC/H2020/957254//DevOps for Complex Cyber-physical Systems/COSMOS	de_CH
zhaw.issue	111047	de_CH
zhaw.originated.zhaw	Yes	de_CH
zhaw.publication.status	publishedVersion	de_CH
zhaw.volume	181	de_CH
zhaw.publication.review	Peer review (Publikation)	de_CH
zhaw.funding.snf	181973	de_CH
zhaw.webfeed	Software Systems	de_CH
zhaw.funding.zhaw	COSMOS – DevOps for Complex Cyber-physical Systems of Systems	de_CH
zhaw.author.additional	No	de_CH
zhaw.display.portrait	Yes	de_CH
Appears in collections:	Publikationen School of Engineering

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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.