SEMKIS-DSL: A Domain-Specific Language to Support Requirements Engineering of Datasets and Neural Network Recognition

Neural network (NN) components are being increasingly incorporated into software systems. Neural network properties are determined by their architecture, as well as the training and testing datasets used. The engineering of datasets and neural networks is a challenging task that requires methods and...

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Bibliographic Details
Published inInformation (Basel) Vol. 14; no. 4; p. 213
Main Authors Jahić, Benjamin, Guelfi, Nicolas, Ries, Benoît
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.04.2023
Subjects
Automation
Customer satisfaction
Data collection
Datasets
Deep learning
Design
Digital Subscriber Line
Domain specific languages
domain-specific language
Engineering
Engineers
Handwriting recognition
Job qualifications
Language
method
Methods
model-driven engineering
neural network
Neural networks
requirements
Requirements analysis
Requirements specifications
Skills
Software
Software engineering
Specification and description languages
Subject specialists
Training
Vocational qualifications
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Summary:Neural network (NN) components are being increasingly incorporated into software systems. Neural network properties are determined by their architecture, as well as the training and testing datasets used. The engineering of datasets and neural networks is a challenging task that requires methods and tools to satisfy customers’ expectations. The lack of tools that support requirements specification languages makes it difficult for engineers to describe dataset and neural network recognition skill requirements. Existing approaches often rely on traditional ad hoc approaches, without precise requirement specifications for data selection criteria, to build these datasets. Moreover, these approaches do not focus on the requirements of the neural network’s expected recognition skills. We aim to overcome this issue by defining a domain-specific language that precisely specifies dataset requirements and expected recognition skills after training for an NN-based system. In this paper, we present a textual domain-specific language (DSL) called SEMKIS-DSL (Software Engineering Methodology for the Knowledge management of Intelligent Systems) that is designed to support software engineers in specifying the requirements and recognition skills of neural networks. This DSL is proposed in the context of our general SEMKIS development process for neural network engineering. We illustrate the DSL’s concepts using a running example that focuses on the recognition of handwritten digits. We show some requirements and recognition skills specifications and demonstrate how our DSL improves neural network recognition skills.
ISSN:2078-2489
2078-2489
DOI:10.3390/info14040213