Semantic Clustering of Functional Requirements Using Agglomerative Hierarchical Clustering

Salman, Hamzeh Eyal; Hammad, Mustafa; Seriai, Abdelhak-Djamel; Al-Sbou, Ahed Mleih

doi:10.3390/info9090222

Cited by 21 publications

(23 citation statements)

References 28 publications

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“…The proposed process model of data-driven PSS should be further validated by applying the visualization platform as a data-driven PSS developer to communicate with stakeholders. Stakeholders' requests can be classified using machine learning algorithms such as classification or natural language processing (Eyal Salman et al, 2018;Li et al, 2018). Future research will also extend the system boundary by applying the MOO method to infrastructure systems including mobility, energy, water, and their nexus with the built environment.…”

Section: Resultsmentioning

confidence: 99%

Data-driven planning support system for a campus design

Yang

Chang

Saha

et al. 2020

Environment and Planning B: Urban Analytics and City Science

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The paper aims to develop a campus-level planning support system that is driven by data analytics by comparing two design approaches, anticipation and optimization. A campus is defined as a small-scale complex urban system of buildings and infrastructure. Three questions are addressed: (1) What generates campus design? What principles are taken for making design decisions? (2) How do we optimize design options based on multi-criteria performance and multi-objectives? (3) How can we manage a process of complex systems design, from scenario making, performance evaluation, design optimization to design generation? What properties can be derived from the above processes to inform campus design decisions? Driven by the above questions, design approaches by anticipation and by optimization were employed in a campus site design. By reviewing those processes, a data-driven campus planning support system is proposed to manage complex decisions and communicate design decisions through a visualization platform. This research will contribute to exploring how urban design is driven by data analytics for promoting energy efficiency and system resilience.

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Section: Resultsmentioning

confidence: 99%

Data-driven planning support system for a campus design

Yang

Chang

Saha

et al. 2020

Environment and Planning B: Urban Analytics and City Science

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“…Several classification algorithms (like [1,33]) can be used to achieve classification of FRs and NFRs. Some of the researches are based on clustering algorithms to classify FRs and NFRs [12]. On the other hand, Knauss et.…”

Section: Contextmentioning

confidence: 99%

Sentence Embedding Models for Similarity Detection of Software Requirements

et al. 2021

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Semantic similarity detection mainly relies on the availability of laboriously curated ontologies, as well as of supervised and unsupervised neural embedding models. In this paper, we present two domain-specific sentence embedding models trained on a natural language requirements dataset in order to derive sentence embeddings specific to the software requirements engineering domain. We use cosine-similarity measures in both these models. The result of the experimental evaluation confirm that the proposed models enhance the performance of textual semantic similarity measures over existing state-of-the-art neural sentence embedding models: we reach an accuracy of 88.35%-which improves by about 10% on existing benchmarks.

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“…The library pattern, on the other hand, is not concerned with the use of healthcare requirements. Hamzeh Eyal Salman in [21], designed an approach to cluster functional requirements automatically based on semantic measure Using Agglomerative Hierarchical Clustering (AHC) by grouping similar functional requirements into clusters. Results achieved high performance according to a well-known measure and didn't apply yet for the clinical system.…”

Section: Literature Reviewmentioning

confidence: 99%

An Approach for Requirements Engineering Analysis using Conceptual Mapping in Healthcare Domain

Radwan¹,

Abdo²,

Gaber³

2021

IJACSA

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Healthcare systems aim to achieve the best possible support for patient care and to provide good medical care. Good analysis of requirements is essential to avoid any crises. Elicitation of healthcare systems requirements is an emerging and critical phase. It is a challenging task to deal with constraints from the stakeholders and restrictions of the legal issues. In this research, an approach "Conceptual Mapping for non-functional Health care Requirements; CMHR" is proposed to perform an analysis and to evaluate the relationship between the clinical nonfunctional requirements of medical devices (ventilators as an example in this research) according to the following five attributes: prioritization of requirements, suitability, feasibility, achievability, and risky. Requirements are automatically clustered using the K-means++ algorithm to find out the optimal number of clusters. Requirements are then clustered to visualize the concept map. Clustering is applied on different combinations of the attributes to sort the requirements and to visualize them. Label names are assigned to the classes of requirements to assign each requirement to the appropriate class. Consequently, a prediction of a new requirement can be figured automatically. The approach achieved less rework, fast delivery of the project with good quality, and achieved a higher level of user satisfaction.

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Semantic Clustering of Functional Requirements Using Agglomerative Hierarchical Clustering

Cited by 21 publications

References 28 publications

Data-driven planning support system for a campus design

Data-driven planning support system for a campus design

Sentence Embedding Models for Similarity Detection of Software Requirements

An Approach for Requirements Engineering Analysis using Conceptual Mapping in Healthcare Domain

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