Stop Words for Processing Software Engineering Documents: Do they Matter?
Abstract
Stop words, which are considered non-predictive, are often eliminated in natural language processing tasks. However, the definition of uninformative vocabulary is vague, so most algorithms use general knowledge-based stop lists to remove stop words. There is an ongoing debate among academics about the usefulness of stop word elimination, especially in domain-specific settings. In this work, we investigate the usefulness of stop word removal in a software engineering context. To do this, we replicate and experiment with three software engineering research tools from related work. Additionally, we construct a corpus of software engineering domain-related text from 10,000 Stack Overflow questions and identify 200 domain-specific stop words using traditional information-theoretic methods. Our results show that the use of domain-specific stop words significantly improved the performance of research tools compared to the use of a general stop list and that 17 out of 19 evaluation measures showed better performance. Online appendix: https://zenodo.org/record/7865748
Cite
@article{arxiv.2303.10439,
title = {Stop Words for Processing Software Engineering Documents: Do they Matter?},
author = {Yaohou Fan and Chetan Arora and Christoph Treude},
journal= {arXiv preprint arXiv:2303.10439},
year = {2023}
}
Comments
Accepted for publication at the 2nd Intl. Workshop on NL-based Software Engineering (NLBSE 2023)