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

• Published in: Information (Basel) Vol. 14; no. 4; p. 213
• Main Authors: Jahić, Benjamin, Guelfi, Nicolas, Ries, Benoît
• Format: Journal Article
• Language: English
• 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
• ISSN: 2078-2489; 2078-2489
• DOI: 10.3390/info14040213

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.