Stop words for processing software engineering documents: Do they matter

• Main Authors: FAN, Yaohou, ARORA, Chetan, TREUDE, Christoph
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Natural Language Processing (NLP); Software Engineering Documents; Stop Words; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/8912; https://ink.library.smu.edu.sg/context/sis_research/article/9915/viewcontent/stop.pdf
• Institution: Singapore Management University

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 domainspecific 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.