Requirements Engineering aims at supporting the understanding of the purpose of a software system to be built, and at keeping the whole design and development process aligned with it. Research in Requirements Engineering (RE) provides methods and techniques to support various activities in the requirements life-cycle, from requirements elicitation to requirements verification and validation. Artificial Intelligence (AI) techniques, are more and more exploited in such methods, including natural language processing techniques, since many RE artefacts are expressed as natural language text; techniques based on optimisation, machine learning, and deep learning with the objective of improving the efficiency of the analysts and decision-makers performing RE activities. In this chapter we focus on two specific use cases in RE, namely requirements elicitation form textual user feedback, and requirements prioritisation. We present solutions to the two problems based on AI techniques, specifically machine learning, natural Language processing, and Genetic Algorithms. The application of the proposed methods in industrial contexts allowed us to validate their usefulness in terms of increased efficiency of the organ- isations during their decision-making processes. Finally, we discuss the use cases in the broader context of the RE management process, highlighting opportunities and limits of the AI approaches and current trends in the use of AI in RE.

Requirements Engineering

Kifetew, Fitsum;Perini, Anna;Susi, Angelo
2023-01-01

Abstract

Requirements Engineering aims at supporting the understanding of the purpose of a software system to be built, and at keeping the whole design and development process aligned with it. Research in Requirements Engineering (RE) provides methods and techniques to support various activities in the requirements life-cycle, from requirements elicitation to requirements verification and validation. Artificial Intelligence (AI) techniques, are more and more exploited in such methods, including natural language processing techniques, since many RE artefacts are expressed as natural language text; techniques based on optimisation, machine learning, and deep learning with the objective of improving the efficiency of the analysts and decision-makers performing RE activities. In this chapter we focus on two specific use cases in RE, namely requirements elicitation form textual user feedback, and requirements prioritisation. We present solutions to the two problems based on AI techniques, specifically machine learning, natural Language processing, and Genetic Algorithms. The application of the proposed methods in industrial contexts allowed us to validate their usefulness in terms of increased efficiency of the organ- isations during their decision-making processes. Finally, we discuss the use cases in the broader context of the RE management process, highlighting opportunities and limits of the AI approaches and current trends in the use of AI in RE.
2023
978-981-19-9947-5
978-981-19-9948-2
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11582/340647
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