  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

Software Engineering;Computation and Language