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An Ensemble Approach for Annotating Source Code Identifiers with Part-of-speech Tags

Authors :
Newman, Christian D.
Decker, Michael J.
AlSuhaibani, Reem S.
Peruma, Anthony
Mohapatra, Satyajit
Vishnoi, Tejal
Zampieri, Marcos
Mkaouer, Mohamed W.
Sheldon, Timothy J.
Hill, Emily
Source :
in IEEE Transactions on Software Engineering, vol. , no. 01, pp. 1-1, 5555
Publication Year :
2021

Abstract

This paper presents an ensemble part-of-speech tagging approach for source code identifiers. Ensemble tagging is a technique that uses machine-learning and the output from multiple part-of-speech taggers to annotate natural language text at a higher quality than the part-of-speech taggers are able to obtain independently. Our ensemble uses three state-of-the-art part-of-speech taggers: SWUM, POSSE, and Stanford. We study the quality of the ensemble's annotations on five different types of identifier names: function, class, attribute, parameter, and declaration statement at the level of both individual words and full identifier names. We also study and discuss the weaknesses of our tagger to promote the future amelioration of these problems through further research. Our results show that the ensemble achieves 75\% accuracy at the identifier level and 84-86\% accuracy at the word level. This is an increase of +17\% points at the identifier level from the closest independent part-of-speech tagger.<br />Comment: 18 pages. arXiv admin note: text overlap with arXiv:2007.08033

Details

Database :
arXiv
Journal :
in IEEE Transactions on Software Engineering, vol. , no. 01, pp. 1-1, 5555
Publication Type :
Report
Accession number :
edsarx.2109.00629
Document Type :
Working Paper
Full Text :
https://doi.org/10.1109/TSE.2021.3098242