Semantic Analysis of Functional and Non-Functional Requirements in Software Requirements Specifications

Rashwan, Abderahman

doi:10.1007/978-3-642-30353-1_42

Cited by 4 publications

(4 citation statements)

References 6 publications

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“…In the case of Navarro‐Almanza et al [8 ] used convolutional neural networks with a word2vec approach for software requirements classification, and they achieved a maximum accuracy of 81%. Likewise, Rashwan [16 ] used a two‐layer neural network that processes text with word2vec, and they performed a maximum accuracy of 77%. The proposal of Kurtanovíc and Maalej [5 ] used an SVM with lexical, syntactical, and meta‐data feature types.…”

Section: Discussionmentioning

confidence: 99%

“…Most approaches reported in the literature [5, 6, 8, 16 ] focus on classifying FRs and NFRs for software quality purposes, whereas our approach analyses design‐level requirements (for supporting the work of UI designers). Design‐level requirements are rarely taken into consideration in current datasets if any.…”

Section: Discussionmentioning

confidence: 99%

“…While Tarawneh [6 ] presented a new technique for software requirements classification using latent semantic analysis (LSA) that consists of file representation and the cosine similarity measure. Rashwan [16 ] processed NFR through a two‐layer neural network that vectorises words (word2vec). To eliminate unnecessary content and extract relevant features.…”

Section: Related Workmentioning

confidence: 99%

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Classifying design‐level requirements using machine learning for a recommender of interaction design patterns

Silva-Rodríguez

Nava-Muñoz

Castro³

et al. 2020

IET softw.

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Software Engineering is a discipline that encompasses processes associated with the development of interactive systems. The perceived quality of an interactive system is heavily influenced by the user interface design, which may result in many challenges. One such challenge is design‐level requirements analysis. The success of the software system is mostly dependent on how well users’ requirements have been understood and translated into appropriate functionalities. During the interactive system design process, it is common to find recurring problems in human–computer interactions, for which reusing solutions is highly feasible. Interaction design patterns seek to support designers in decision making during the design of interactive systems. Due to the design task tends to be subjective and prone to errors. This work aims at presenting and evaluating an interaction design patterns recommendation model based on design‐level requirements classification, through the application of supervised machine learning algorithms. To compare the performance of four classification algorithms, a study was carried out, in which the linear support vector machine was the most suitable to this problem. The results of this work can be used for implementing frameworks that can better support designers’ decision making when designing user interfaces.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Classifying design‐level requirements using machine learning for a recommender of interaction design patterns

Silva-Rodríguez

Nava-Muñoz

Castro³

et al. 2020

IET softw.

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show abstract

“…Indeed, the distinction between functional and non-functional requirements is not a clear-cut separation where a non-functional requirement, such as security, may generate multi-functional requirement related to security services [1]. Therefore, a number of attempts are proposed to automate the identification of such distinction in [18,[27][28][29].…”

Section: Other Related Workmentioning

confidence: 99%

Semantic Clustering of Functional Requirements Using Agglomerative Hierarchical Clustering

et al. 2018

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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.

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Assessment and evaluation of requirements elicitation techniques using analysis determination requirements framework

Besrour

Rahim

Dominic

2014

2014 International Conference on Computer and Information Sciences (ICCOINS)

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Semantic Analysis of Functional and Non-Functional Requirements in Software Requirements Specifications

Cited by 4 publications

References 6 publications

Classifying design‐level requirements using machine learning for a recommender of interaction design patterns

Classifying design‐level requirements using machine learning for a recommender of interaction design patterns

Semantic Clustering of Functional Requirements Using Agglomerative Hierarchical Clustering

Assessment and evaluation of requirements elicitation techniques using analysis determination requirements framework

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