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A Comparison of Recent Requirements Gathering and Management Tools in Requirements Engineering for IoT-Enabled Sustainable Cities

Author

Listed:
  • Muhammad Asgher Nadeem

    (Department of Computer Science and Engineering, Hanyang University, Ansan 15588, Korea)

  • Scott Uk-Jin Lee

    (Department of Computer Science and Engineering, Hanyang University, Ansan 15588, Korea)

  • Muhammad Usman Younus

    (Department of Computer Science and IT, University of Jhang, Jhang 35200, Pakistan
    Ecole Doctorale Mathematiques, Informatique, Telecommunications de Toulouse, University Paul Sabatier, 31330 Toulouse, France)

Abstract

The Internet of Things (IoT) is a paradigm that facilitates the proliferation of different devices such as sensors and Radio Frequency Identification (RFIDs) for real-time applications such as healthcare and sustainable cities. The growing popularity of IoT opens up new possibilities, and one of the most notable applications is related to the evolving sustainable city paradigm. A sustainable city is normally designed in such a way to consider the environmental impact and a social, economic, and resilient habitat for existing populations without compromising the ability of future generations to experience the same, while the process of managing project requirements is known as requirements management. To design a high-quality project, effective requirements management is imperative. A number of techniques are already available to perform the requirement gathering process, and software developers apply them to collect the requirements. Nevertheless, they are facing many issues in gathering requirements due to a lack of literature on the selection of appropriate methods, which affects the quality of the software. The software design quality can be improved by using requirements capture and management techniques. Some tools are used to comprehend the system accurately. In this paper, a qualitative comparison of requirements-gathering tools using Artificial Intelligence (AI) and requirements-management tools is presented for sustainable cities. With all the tools and techniques available for capturing and managing requirements, it has been proven that software developers have a wide range of alternatives for selecting the best tool that fits their needs, such as chosen by the AI agent. This effort will aid in the development of requirements for IoT-enabled sustainable cities.

Suggested Citation

  • Muhammad Asgher Nadeem & Scott Uk-Jin Lee & Muhammad Usman Younus, 2022. "A Comparison of Recent Requirements Gathering and Management Tools in Requirements Engineering for IoT-Enabled Sustainable Cities," Sustainability, MDPI, vol. 14(4), pages 1-18, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:4:p:2427-:d:753919
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    References listed on IDEAS

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    1. Thai-Minh Truong & Lam-Son Lê & Elda Paja & Paolo Giorgini, 2021. "A data-driven, goal-oriented framework for process-focused enterprise re-engineering," Information Systems and e-Business Management, Springer, vol. 19(2), pages 683-747, June.
    2. Tan Yigitcanlar & Rashid Mehmood & Juan M. Corchado, 2021. "Green Artificial Intelligence: Towards an Efficient, Sustainable and Equitable Technology for Smart Cities and Futures," Sustainability, MDPI, vol. 13(16), pages 1-14, August.
    3. Aslam, Sheraz & Herodotou, Herodotos & Mohsin, Syed Muhammad & Javaid, Nadeem & Ashraf, Nouman & Aslam, Shahzad, 2021. "A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    4. Basit Shahzad & Iqra Javed & Asadullah Shaikh & Adel Sulaiman & Ahsanullah Abro & Muhammad Ali Memon, 2021. "Reliable Requirements Engineering Practices for COVID-19 Using Blockchain," Sustainability, MDPI, vol. 13(12), pages 1-25, June.
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