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Replication in Requirements Engineering: The NLP for RE Case
University of Luxembourg, Luxembourg.
Utrecht University, Netherlands.
Utrecht University, Netherlands.
Utrecht University, Netherlands.
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2024 (English)In: ACM Transactions on Software Engineering and Methodology, ISSN 1049-331X, E-ISSN 1557-7392, Vol. 33, no 6, article id 151Article in journal (Refereed) Published
Abstract [en]

Natural language processing (NLP) techniques have been widely applied in the requirements engineering (RE) field to support tasks such as classification and ambiguity detection. Despite its empirical vocation, RE research has given limited attention to replication of NLP for RE studies. Replication is hampered by several factors, including the context specificity of the studies, the heterogeneity of the tasks involving NLP, the tasks’ inherent hairiness, and, in turn, the heterogeneous reporting structure. To address these issues, we propose a new artifact, referred to as ID-Card, whose goal is to provide a structured summary of research papers emphasizing replication-relevant information. We construct the ID-Card through a structured, iterative process based on design science. In this article: (i) we report on hands-on experiences of replication; (ii) we review the state-of-the-art and extract replication-relevant information: (iii) we identify, through focus groups, challenges across two typical dimensions of replication: data annotation and tool reconstruction; and (iv) we present the concept and structure of the ID-Card to mitigate the identified challenges. This study aims to create awareness of replication in NLP for RE. We propose an ID-Card that is intended to foster study replication but can also be used in other contexts, e.g., for educational purposes. © 2024 Copyright held by the owner/author(s).

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2024. Vol. 33, no 6, article id 151
Keywords [en]
annotation, ID card, Natural Language Processing (NLP), replication, Requirements Engineering (RE), tool reconstruction, Employment, Iterative methods, Natural language processing systems, ID cards, Language processing, Language processing techniques, Natural language processing, Natural languages, Requirement engineering, Requirements engineering
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:bth-26775DOI: 10.1145/3658669ISI: 001283366800012Scopus ID: 2-s2.0-85198652329OAI: oai:DiVA.org:bth-26775DiVA, id: diva2:1887560
Part of project
SERT- Software Engineering ReThought, Knowledge Foundation
Funder
Knowledge Foundation, 20180010Available from: 2024-08-08 Created: 2024-08-08 Last updated: 2025-09-30Bibliographically approved

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Fucci, Davide

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Citation style
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