Advancements and innovations in requirements elicitation: Developing a comprehensive conceptual model

Advancements and innovations in requirements elicitation: Developing a comprehensive conceptual model

Received on 11 March 2024; revised on 20 April 2024; accepted on 22 April 2024 | Oladapo Adeboye Popoola, Henry Ejiga Adama, Chukwuekem David Okeke, Abiodun Emmanuel Akinoso
The article "Advancements and Innovations in Requirements Elicitation: Developing a Comprehensive Conceptual Model" by Oladapo Adeboye Popoola et al. addresses the challenges and advancements in the requirements elicitation process in software development. The authors propose a comprehensive conceptual model that integrates recent technological advancements and innovations to enhance the accuracy, efficiency, and effectiveness of requirements elicitation. Key advancements include the use of Natural Language Processing (NLP) for automated analysis of textual requirements, Machine Learning (ML) for predicting and managing evolving requirements, and Human-Computer Interaction (HCI) for improving stakeholder engagement and collaboration. The model also emphasizes context-awareness, addressing the dynamic nature of requirements by considering organizational, cultural, and environmental contexts. Additionally, it introduces techniques for managing conflicting requirements through prioritization and negotiation, and highlights the importance of iterative refinement and validation throughout the development lifecycle. The article concludes that these advancements and innovations are crucial for developing high-quality software systems and ensuring successful project outcomes.The article "Advancements and Innovations in Requirements Elicitation: Developing a Comprehensive Conceptual Model" by Oladapo Adeboye Popoola et al. addresses the challenges and advancements in the requirements elicitation process in software development. The authors propose a comprehensive conceptual model that integrates recent technological advancements and innovations to enhance the accuracy, efficiency, and effectiveness of requirements elicitation. Key advancements include the use of Natural Language Processing (NLP) for automated analysis of textual requirements, Machine Learning (ML) for predicting and managing evolving requirements, and Human-Computer Interaction (HCI) for improving stakeholder engagement and collaboration. The model also emphasizes context-awareness, addressing the dynamic nature of requirements by considering organizational, cultural, and environmental contexts. Additionally, it introduces techniques for managing conflicting requirements through prioritization and negotiation, and highlights the importance of iterative refinement and validation throughout the development lifecycle. The article concludes that these advancements and innovations are crucial for developing high-quality software systems and ensuring successful project outcomes.
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[slides and audio] Advancements and innovations in requirements elicitation%3A Developing a comprehensive conceptual model