Multi-label Classifier to Deal with Misclassification in Non-functional Requirements

Sabir, Maliha; Chrysoulas, Christos; Banissi, Ebad

doi:10.1007/978-3-030-45688-7_49

Cited by 5 publications

(2 citation statements)

References 31 publications

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“…In [12], a multi-label requirement classifier based on CNN, classified NFRs into five categories: reliability, efficiency, portability, usability, and maintainability. Researchers have given less attention to FR work, referenced in fewer journals than NFR [13].…”

Section: Related Workmentioning

confidence: 99%

A Non-Exclusive Multi-Class Convolutional Neural Network for the Classification of Functional Requirements in AUTOSAR Software Requirement Specification Text

Menon²,

Atul

2022

IEEE Access

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Software Requirement Specification (SRS) describes a software system to be developed that captures the functional, non-functional, and technical aspects of the stakeholder's requirements. Retrieval and extraction of software information from SRS are essential to the development of software product line (SPL). Albeit Natural Language Processing (NLP) techniques, such as information retrieval and standard machine learning, have been advocated in the recent past as a semi-automatic means of optimising requirements specifications, they have not been widely embraced. The complexity in the organization's information makes requirement analysis intricately a challenging task. The interdependence of subsystems and within an organisation drives this complexity. A plain multi-class classification framework may not address this issue. Hence, this paper propounds an automated non-exclusive approach for classification of functional requirements from SRS, using a deep learning framework. Specifically, Word2Vec and FastText word embeddings are utilised for document representation for training a convolutional neural network (CNN). The study was carried out by the compilation of manually categorised relevant enterprise data (AUTomotive Open System ARchitecture (AUTOSAR)), which were also employed for model training. Over a convolutional neural network, the impact of data trained with Word2Vec and FastText word embeddings from SRS documentation were compared to pre-trained word embeddings models, available online.

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Section: Related Workmentioning

confidence: 99%

A Non-Exclusive Multi-Class Convolutional Neural Network for the Classification of Functional Requirements in AUTOSAR Software Requirement Specification Text

Menon²,

Atul

2022

IEEE Access

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“…The non-functional requirements classes considered by some studies are very few (4,23,31). The dataset contains limited non-functional requirement sentences for training and testing purposes (2,3,9). Studies have mostly considered classifiers, which are developed using single machine learning algorithms (8).…”

Section: Non-functional Requirements Classification Using Convolution...mentioning

confidence: 99%

Classification of Non-Functional Requirements From IoT Oriented Healthcare Requirement Document

Khurshid

Imtiaz

Boulila

et al. 2022

Front. Public Health

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Internet of Things (IoT) involves a set of devices that aids in achieving a smart environment. Healthcare systems, which are IoT-oriented, provide monitoring services of patients' data and help take immediate steps in an emergency. Currently, machine learning-based techniques are adopted to ensure security and other non-functional requirements in smart health care systems. However, no attention is given to classifying the non-functional requirements from requirement documents. The manual process of classifying the non-functional requirements from documents is erroneous and laborious. Missing non-functional requirements in the Requirement Engineering (RE) phase results in IoT oriented healthcare system with compromised security and performance. In this research, an experiment is performed where non-functional requirements are classified from the IoT-oriented healthcare system's requirement document. The machine learning algorithms considered for classification are Logistic Regression (LR), Support Vector Machine (SVM), Multinomial Naive Bayes (MNB), K-Nearest Neighbors (KNN), ensemble, Random Forest (RF), and hybrid KNN rule-based machine learning (ML) algorithms. The results show that our novel hybrid KNN rule-based machine learning algorithm outperforms others by showing an average classification accuracy of 75.9% in classifying non-functional requirements from IoT-oriented healthcare requirement documents. This research is not only novel in its concept of using a machine learning approach for classification of non-functional requirements from IoT-oriented healthcare system requirement documents, but it also proposes a novel hybrid KNN-rule based machine learning algorithm for classification with better accuracy. A new dataset is also created for classification purposes, comprising requirements related to IoT-oriented healthcare systems. However, since this dataset is small and consists of only 104 requirements, this might affect the generalizability of the results of this research.

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A Deep Learning-Based Framework for the Classification of Non-functional Requirements

Sabir

Banissi

Child

2021

Advances in Intelligent Systems and Computing

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Multi-label Classifier to Deal with Misclassification in Non-functional Requirements

Cited by 5 publications

References 31 publications

A Non-Exclusive Multi-Class Convolutional Neural Network for the Classification of Functional Requirements in AUTOSAR Software Requirement Specification Text

A Non-Exclusive Multi-Class Convolutional Neural Network for the Classification of Functional Requirements in AUTOSAR Software Requirement Specification Text

Classification of Non-Functional Requirements From IoT Oriented Healthcare Requirement Document

A Deep Learning-Based Framework for the Classification of Non-functional Requirements

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