Semantic Clustering of Functional Requirements Using Agglomerative Hierarchical Clustering

Hamzeh Eyal Salman 1 Mustafa Hammad 1 Abdelhak-Djamel Seriai 2 Ahed Al-Sbou 3
1 Mutah University [Jordanie]
2 MAREL - Models And Reuse Engineering, Languages
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
3 Al-Hussein Bin Talal University [Jordanie]
Abstract : Software applications have become a fundamental part in the daily work of modern society as they meet different needs of users in different domains. Such needs are known as software requirements (SRs) which are separated into functional (software services) and non-functional (quality attributes). The first step of every software development project is SR elicitation. This step is a challenge task for developers as they need to understand and analyze SRs manually. For example, the collected functional SRs need to be categorized into different clusters to break-down the project into a set of sub-projects with related SRs and devote each sub-project to a separate development team. However, functional SRs clustering has never been considered in the literature. Therefore, in this paper, we propose an approach to automatically cluster functional requirements based on semantic measure. An empirical evaluation is conducted using four open-access software projects to evaluate our proposal. The experimental results demonstrate that the proposed approach identifies semantic clusters according to well-known used measures in the subject.
Keywords : Requirements elicitation Functional requirements Semantic clustering Hierarchical clustering Software requirement specifications
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Journal articles
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01932795
Contributor : Abdelhak-Djamel Seriai <>
Submitted on : Friday, November 23, 2018 - 1:02:59 PM
Last modification on : Thursday, February 7, 2019 - 5:18:54 PM

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Hamzeh Eyal Salman, Mustafa Hammad, Abdelhak-Djamel Seriai, Ahed Al-Sbou. Semantic Clustering of Functional Requirements Using Agglomerative Hierarchical Clustering. Journal of Information, Information Technology, and Organizations, Informing Science Institute, 2018, Special Issue: Information Technology: New Generations (ITNG), 9 (9), pp.222-239. ⟨10.3390/info9090222⟩. ⟨lirmm-01932795⟩

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