arXiv · 2504.17038
SCALAR: A Part-of-speech Tagger for Identifiers
Abstract
The paper presents the Source Code Analysis and Lexical Annotation Runtime (SCALAR), a tool specialized for mapping (annotating) source code identifier names to their corresponding part-of-speech tag sequence (grammar pattern). SCALAR's internal model is trained using scikit-learn's GradientBoostingClassifier in conjunction with a manually-curated oracle of identifier names and their grammar patterns. This specializes the tagger to recognize the unique structure of the natural language used by developers to create all types of identifiers (e.g., function names, variable names etc.). SCALAR's output is compared with a previous version of the tagger, as well as a modern off-the-shelf part-of-speech tagger to show how it improves upon other taggers' output for annotating identifiers. The code is available on Github
Explore related subjects
Keep this discovery
Christian D. Newman, Brandon Scholten, Sophia Testa, Joshua A. C. Behler, Syreen Banabilah, Michael L. Collard, Michael J. Decker, Mohamed Wiem Mkaouer, Marcos Zampieri, Eman Abdullah AlOmar, Reem Alsuhaibani, Anthony Peruma, Jonathan I. Maletic. 2025-04-23. SCALAR: A Part-of-speech Tagger for Identifiers. https://arxiv.org/abs/2504.17038
Cite the original work for its findings. Save a collection to share your selection of sources.