Towards a Data Engineering Process in Data-Driven Systems Engineering

Petersen, Patrick; Stage, Hanno; Langner, Jacob; Ries, Lennart; Rigoll, Philipp; Hohl, Carl Philipp; Sax, Eric

doi:10.1109/isse54508.2022.10005441

Cited by 6 publications

(1 citation statement)

References 27 publications

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“…Software requirements engineering activities must be able to run correctly, completely and accurately so that the information system developed does not become backward, over budget, or even fails to be completed 6 . The quality of the requirement engineering process is an important factor that can cause errors in software engineering projects 7 .…”

Section: Introductionmentioning

confidence: 99%

Natural Language Processing For Requirement Elicitation In University Using Kmeans And Meanshift Algorithm

Bernanda,

Jawawi,

Abd Halim

et al. 2024

Baghdad Sci.J

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Data Driven Requirement Engineering (DDRE) represents a vision for a shift from the static traditional methods of doing requirements engineering to dynamic data-driven user-centered methods. Data available and the increasingly complex requirements of system software whose functions can adapt to changing needs to gain the trust of its users, an approach is needed in a continuous software engineering process. This need drives the emergence of new challenges in the discipline of requirements engineering to meet the required changes. The problem in this study was the method in data discrepancies which resulted in the needs elicitation process being hampered and in the end software development found discrepancies and could not meet the needs of stakeholders and the goals of the organization. The research objectives in this research to the process collected and integrating data from multiple sources and ensuring interoperability. Conclusion in this research is determining is the clustering algorithm help the collection data and elicitation process has a somewhat greater impact on the ratings provided by professionals for pairs that belong to the same cluster. However, the influence of POS tagging on the ratings given by professionals is relatively consistent for pairs within the same cluster and pairs in different clusters.

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Section: Introductionmentioning

confidence: 99%