Applying i* in Conceptual Modelling in Machine Learning
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http://hdl.handle.net/10045/118806
Title: | Applying i* in Conceptual Modelling in Machine Learning |
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Authors: | Barrera, Jose Manuel | Reina Reina, Alejandro | Maté, Alejandro | Trujillo, Juan |
Research Group/s: | Lucentia |
Center, Department or Service: | Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos |
Keywords: | Machine learning | iStar | Requirements engineering | Conceptual modelling | Methodology |
Knowledge Area: | Lenguajes y Sistemas Informáticos |
Issue Date: | Oct-2021 |
Publisher: | CEUR |
Citation: | Proceedings of the 14th International iStar Workshop, October 18-21, 2021, St. Johns (NL), Canada. CEUR Workshop Proceedings, Vol-2983, 56-62 |
Abstract: | The i* framework is a popular and well-equipped technique for capturing the organizational environment and requirements of a system. However, i* heavily depends on the designer experience to cope with the idiosyncrasy of each specific field. While the machine learning field would benefit from a requirements representation, its complexity makes it unfeasible to directly use i*. The large number of constructs and nuances between elements puts a severe strain on the designer, leading to the creation of error-prone models. Therefore, in order to tackle this problem, we present an extension of i*. Our proposal covers the main gaps between machine learning and conceptual modeling with the aim of creating a suitable baseline methodology for machine learning requirements engineering. The advantage of our proposal is that our language specifies the main elements involved in machine learning models and constrains their interactions, filtering invalid designs and thus reducing the burden of knowledge while making the process less error-prone. |
URI: | http://hdl.handle.net/10045/118806 |
ISSN: | 1613-0073 |
Language: | eng |
Type: | info:eu-repo/semantics/conferenceObject |
Rights: | © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). |
Peer Review: | si |
Publisher version: | http://ceur-ws.org/Vol-2983/ |
Appears in Collections: | INV - LUCENTIA - Comunicaciones a Congresos, Conferencias, etc. |
Files in This Item:
File | Description | Size | Format | |
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Barrera_etal_2021_CEUR.pdf | 1,8 MB | Adobe PDF | Open Preview | |
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