An Ensemble Approach for Annotating Source Code Identifiers with Part-of-speech Tags
This work addresses the challenge of improving natural language processing for software engineering by enhancing identifier annotation, though it is incremental as it builds on existing taggers.
The paper tackled the problem of part-of-speech tagging for source code identifiers by developing an ensemble method using three state-of-the-art taggers, achieving 75% accuracy at the identifier level and 84-86% at the word level, with a 17% improvement over the best independent tagger.
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.