# Stop Words for Processing Software Engineering Documents: Do they Matter?

arXiv: https://arxiv.org/abs/2303.10439 · HTML: https://mlsanity.com/paper/2303.10439/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 40 references that are in the map.
- First posted on arXiv 18 Mar 2023.

## Builds on (most cited first, 5 of 5)

- [Stopwords in Technical Language Processing](https://mlsanity.com/paper/2006.02633/) (2020)
- [Categorizing the Content of GitHub README Files](https://mlsanity.com/paper/1802.06997/) (2018)
- [TechNet: Technology Semantic Network Based on Patent Data](https://mlsanity.com/paper/1906.00411/) (2019)
- [Unsupervised Keyword Extraction from Polish Legal Texts](https://mlsanity.com/paper/1408.3731/) (2014)
- [RACK: Code Search in the IDE using Crowdsourced Knowledge](https://mlsanity.com/paper/1807.04479/) (2018)

## Abstract (arXiv)

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