This thesis was conducted by two students as part of their Strategic Information Systems Management degree program at Stockholm University. As presented in this study, the manual elicitation processes in Requirements Engineering are error-prone and time-consuming. Traditional approaches and techniques often produce requirements that are characterized by ambiguity, inadequacy, incompleteness, inconsistency, and obsolescence. The research problem is focused on the lack of clear understanding regarding AI's specific role in supporting the identification of precise and detailed requirements, and the need for summarizing findings of related work. The goal of this thesis is to investigate the impact of AI in Requirements Engineering, focusing primarily on Requirements Elicitation and Analysis. After presenting essential background knowledge of Requirements Engineering, traditional elicitation methods, and Artificial Intelligence, a systematic literature review was performed to unveil Artificial Intelligence methods, techniques, and tools used in Requirements Elicitation and Analysis. With the assistance of the PRISMA methodology, the key findings and results were summarized and presented. The majority of the online literature focused on various issues connected with traditional methods and presented how Artificial Intelligence Chatbots, Text Mining and Natural Language Processing techniques, Virtual Reality, Sentiment Analysis, Crowdsourcing, Deep Learning Techniques, Gamification, and Bayesian Networks are improving the quality and speed of Requirements Elicitation. One of the main challenges faced is that there is no extensive comparison with traditional methods and metrics on how Artificial Intelligence overall helps Requirements Elicitation – only metrics per case. Also, there is not explicit definition regarding which AI methodologies and tools are appropriate for each elicitation and analysis method.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:su-219598 |
| Date | January 2023 |
| Creators | Papapanos, Konstantinos, Pfeifer, Julia |
| Publisher | Stockholms universitet, Institutionen för data- och systemvetenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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