Automatic Generation Method of Airborne Display and Control System Requirement Domain Model Based on NLP

Yu, Mengyuan; Wang, Lisong; Kang, Jiexiang; Gao, Zhongjie; Wang, Hui; Yin, Wei; Cao, Buzhan

doi:10.1109/icccs52626.2021.9449277

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…Madala et al 32 proposed a method to identify model elements in NL requirements using long short-term memory (LSTM) as the classifier. Yu et al 33 proposed a domain model extraction method from requirements for airborne display and control systems based on natural language processing (NLP). Javed et al 34 proposed an automated approach to extract entity relationships and business process models from requirements.…”

Section: Domain Model Constructionmentioning

confidence: 99%

Usefulness of open domain model for identifying missing software requirements concepts

Zhao,

Zhang,

Lian

2023

Softw Pract Exp

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SummaryDetecting missing requirements during software development is crucial to avoid unexpected consequences. However, this task is challenging due to limited domain knowledge of requirements analysts and the dynamic nature of software requirements. Previous studies have shown that requirement‐oriented domain models can help identify omissions in requirements, but they are often incomplete for many domains. Meanwhile, domain models constructed from other artifacts are available online. This raises the question: Can these domain models be useful in identifying missing functional information in requirement specifications? To address this question, we conducted a study to measure the overlap between entities in domain models and requirements. We analyzed the occurrence of overlapped entities, considering four distribution characteristics: the type of entities in the domain model, the distribution of mapped entities in the domain model, the family belonging of the mapped entities in the domain model, and the distribution of mapped entities in the requirements. Based on our findings, we proposed recommendations for missing requirements. Additionally, we performed experiments, including the use of the proposed metric “ancestors of the highest level with the most mapped entities” (AHME). The results showed significant improvements with gains of 146% and 223% in the two domains, highlighting the benefits of these distribution characteristics.

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Section: Domain Model Constructionmentioning

confidence: 99%