A Non-Exclusive Multi-Class Convolutional Neural Network for the Classification of Functional Requirements in AUTOSAR Software Requirement Specification Text
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 softwar...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9931679/ |
| _version_ | 1811312808965439488 |
|---|---|
| author | Sanjanasri Jp Vijay Krishna Menon KP Soman Atul K. R. Ojha |
| author_facet | Sanjanasri Jp Vijay Krishna Menon KP Soman Atul K. R. Ojha |
| author_sort | Sanjanasri Jp |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T10:43:09Z |
| format | Article |
| id | doaj.art-03b49034e84a40ac8488fd72342fa902 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T10:43:09Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-03b49034e84a40ac8488fd72342fa9022022-12-22T02:49:51ZengIEEEIEEE Access2169-35362022-01-011011770711771410.1109/ACCESS.2022.32177529931679A Non-Exclusive Multi-Class Convolutional Neural Network for the Classification of Functional Requirements in AUTOSAR Software Requirement Specification TextSanjanasri Jp0https://orcid.org/0000-0002-2641-3829Vijay Krishna Menon1https://orcid.org/0000-0003-3328-0347KP Soman2Atul K. R. Ojha3Center for Computational Engineering and Networking, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, IndiaKeepFlying, 5 Tampines Central 6, SingaporeCenter for Computational Engineering and Networking, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, IndiaData Science Institute, National University of Ireland Galway, Galway, IrelandSoftware 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.https://ieeexplore.ieee.org/document/9931679/Functional requirementsoftware requirement specificationconvolutional neural networkmulti-layer perceptronword embedding |
| spellingShingle | Sanjanasri Jp Vijay Krishna Menon KP Soman Atul K. R. Ojha A Non-Exclusive Multi-Class Convolutional Neural Network for the Classification of Functional Requirements in AUTOSAR Software Requirement Specification Text IEEE Access Functional requirement software requirement specification convolutional neural network multi-layer perceptron word embedding |
| title | A Non-Exclusive Multi-Class Convolutional Neural Network for the Classification of Functional Requirements in AUTOSAR Software Requirement Specification Text |
| title_full | A Non-Exclusive Multi-Class Convolutional Neural Network for the Classification of Functional Requirements in AUTOSAR Software Requirement Specification Text |
| title_fullStr | A Non-Exclusive Multi-Class Convolutional Neural Network for the Classification of Functional Requirements in AUTOSAR Software Requirement Specification Text |
| title_full_unstemmed | A Non-Exclusive Multi-Class Convolutional Neural Network for the Classification of Functional Requirements in AUTOSAR Software Requirement Specification Text |
| title_short | A Non-Exclusive Multi-Class Convolutional Neural Network for the Classification of Functional Requirements in AUTOSAR Software Requirement Specification Text |
| title_sort | non exclusive multi class convolutional neural network for the classification of functional requirements in autosar software requirement specification text |
| topic | Functional requirement software requirement specification convolutional neural network multi-layer perceptron word embedding |
| url | https://ieeexplore.ieee.org/document/9931679/ |
| work_keys_str_mv | AT sanjanasrijp anonexclusivemulticlassconvolutionalneuralnetworkfortheclassificationoffunctionalrequirementsinautosarsoftwarerequirementspecificationtext AT vijaykrishnamenon anonexclusivemulticlassconvolutionalneuralnetworkfortheclassificationoffunctionalrequirementsinautosarsoftwarerequirementspecificationtext AT kpsoman anonexclusivemulticlassconvolutionalneuralnetworkfortheclassificationoffunctionalrequirementsinautosarsoftwarerequirementspecificationtext AT atulkrojha anonexclusivemulticlassconvolutionalneuralnetworkfortheclassificationoffunctionalrequirementsinautosarsoftwarerequirementspecificationtext AT sanjanasrijp nonexclusivemulticlassconvolutionalneuralnetworkfortheclassificationoffunctionalrequirementsinautosarsoftwarerequirementspecificationtext AT vijaykrishnamenon nonexclusivemulticlassconvolutionalneuralnetworkfortheclassificationoffunctionalrequirementsinautosarsoftwarerequirementspecificationtext AT kpsoman nonexclusivemulticlassconvolutionalneuralnetworkfortheclassificationoffunctionalrequirementsinautosarsoftwarerequirementspecificationtext AT atulkrojha nonexclusivemulticlassconvolutionalneuralnetworkfortheclassificationoffunctionalrequirementsinautosarsoftwarerequirementspecificationtext |