Natural Language Processing (NLP) for Requirements Engineering: a Systematic Mapping Study
Natural Language Processing (NLP) for Requirements Engineering: A Systematic Mapping Study Liping Zhao† Department of Computer Science, The University of Manchester, Manchester, United Kingdom, liping.zhao@manchester.ac.uk Waad Alhoshan Department of Computer Science, The University of Manchester, Manchester, United Kingdom, waad.alhoshan@postgrad.manchester.ac.uk Alessio Ferrari Consiglio Nazionale delle Ricerche, Istituto di Scienza e Tecnologie dell'Informazione "A. Faedo" (CNR-ISTI), Pisa, Italy, alessio.ferrari@isti.cnr.it Keletso J. Letsholo Faculty of Computer Information Science, Higher Colleges of Technology, Abu Dhabi, United Arab Emirates, kletsholo@hct.ac.ae Muideen A. Ajagbe Department of Computer Science, The University of Manchester, Manchester, United Kingdom, muideen.ajagbe@manchester.ac.uk Erol-Valeriu Chioasca Exgence Ltd., CORE, Manchester, United Kingdom, erol@exgence.com Riza T. Batista-Navarro Department of Computer Science, The University of Manchester, Manchester, United Kingdom, riza.batista@manchester.ac.uk † Corresponding author. Context: NLP4RE – Natural language processing (NLP) supported requirements engineering (RE) – is an area of research and development that seeks to apply NLP techniques, tools and resources to a variety of requirements documents or artifacts to support a range of linguistic analysis tasks performed at various RE phases. Such tasks include detecting language issues, identifying key domain concepts and establishing traceability links between requirements. Objective: This article surveys the landscape of NLP4RE research to understand the state of the art and identify open problems. Method: The systematic mapping study approach is used to conduct this survey, which identified 404 relevant primary studies and reviewed them according to five research questions, cutting across five aspects of NLP4RE research, concerning the state of the literature, the state of empirical research, the research focus, the state of the practice, and the NLP technologies used.
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