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Big Data Technologies and Applications. 11th and 12th EAI International Conference, BDTA 2021 and BDTA 2022, Virtual Event, December 2021 and 2022, Proceedings

Research Article

Using Requirements Clustering to Discover Dependent Requirements for Hidden Impact Analysis

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-33614-0_1,
        author={Ahmed Safwat and Mostafa Mohamed Yacoub},
        title={Using Requirements Clustering to Discover Dependent Requirements for Hidden Impact Analysis},
        proceedings={Big Data Technologies and Applications. 11th and 12th EAI International Conference, BDTA 2021 and BDTA 2022, Virtual Event, December 2021 and 2022, Proceedings},
        proceedings_a={BDTA},
        year={2023},
        month={5},
        keywords={ULS ULS Challenges Software Engineering Requirements Engineering NLP Similarity Model Change Management},
        doi={10.1007/978-3-031-33614-0_1}
    }
    
  • Ahmed Safwat
    Mostafa Mohamed Yacoub
    Year: 2023
    Using Requirements Clustering to Discover Dependent Requirements for Hidden Impact Analysis
    BDTA
    Springer
    DOI: 10.1007/978-3-031-33614-0_1
Ahmed Safwat1,*, Mostafa Mohamed Yacoub1
  • 1: Sadat Academy for Management Sciences
*Contact email: asafwat@sadatacademy.edu.eg

Abstract

Since one of the most important practices in any software development lifecycle is Requirements Engineering. Weakly applied RE is one of the main causes for the development breakdown. Main practices of RE processes are specification and elicitation, verification, cooperation, and implementation management. Since ULS systems are increasing in complexity and difficulty, it has observed a growing call for smart modules, methods, and applications that would support improving RE practices. In this paper, we focus on how various kinds of recommendation technologies can be applied to support ULS participants in the completion of the RE tasks; first, we provide an overview of the research related to the application of recommendation tools in RE that was already described in a high level. Second, we show in detail how clustering method as one of Model-based filtering technique can be applied to proactively strengthen “Measuring Change Ripple Effect”, Third, new ideas need to be discussed and future research explored. We have used Natural Language Processing (NLP) and Similarity Models to support the model.

Keywords
ULS ULS Challenges Software Engineering Requirements Engineering NLP Similarity Model Change Management
Published
2023-05-26
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-33614-0_1
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