Recommender systems in model-driven engineering A systematic mapping review
Recommender systems are information filtering systems used in many online applications like music and video broadcasting and e-commerce platforms. They are also increasingly being applied to facilitate software engineering activities. Following this trend, we are witnessing a growing research intere...
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| Published in | Software and systems modeling Vol. 21; no. 1; pp. 249 - 280 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.02.2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1619-1366 1619-1374 |
| DOI | 10.1007/s10270-021-00905-x |
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| Abstract | Recommender systems are information filtering systems used in many online applications like music and video broadcasting and e-commerce platforms. They are also increasingly being applied to facilitate software engineering activities. Following this trend, we are witnessing a growing research interest on recommendation approaches that assist with modelling tasks and model-based development processes. In this paper, we report on a systematic mapping review (based on the analysis of 66 papers) that classifies the existing research work on recommender systems for model-driven engineering (MDE). This study aims to serve as a guide for tool builders and researchers in understanding the MDE tasks that might be subject to recommendations, the applicable recommendation techniques and evaluation methods, and the open challenges and opportunities in this field of research. |
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| AbstractList | Recommender systems are information filtering systems used in many online applications like music and video broadcasting and e-commerce platforms. They are also increasingly being applied to facilitate software engineering activities. Following this trend, we are witnessing a growing research interest on recommendation approaches that assist with modelling tasks and model-based development processes. In this paper, we report on a systematic mapping review (based on the analysis of 66 papers) that classifies the existing research work on recommender systems for model-driven engineering (MDE). This study aims to serve as a guide for tool builders and researchers in understanding the MDE tasks that might be subject to recommendations, the applicable recommendation techniques and evaluation methods, and the open challenges and opportunities in this field of research. |
| Author | Cantador, Iván de Lara, Juan Guerra, Esther Almonte, Lissette |
| Author_xml | – sequence: 1 givenname: Lissette surname: Almonte fullname: Almonte, Lissette email: lissette.almonte@uam.es organization: Modelling and Software Engineering Research Group, Universidad Autónoma de Madrid – sequence: 2 givenname: Esther surname: Guerra fullname: Guerra, Esther organization: Modelling and Software Engineering Research Group, Universidad Autónoma de Madrid – sequence: 3 givenname: Iván surname: Cantador fullname: Cantador, Iván organization: Information Retrieval Group, Universidad Autónoma de Madrid – sequence: 4 givenname: Juan surname: de Lara fullname: de Lara, Juan organization: Modelling and Software Engineering Research Group, Universidad Autónoma de Madrid |
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| Keywords | Model-driven engineering Systematic mapping review Recommender systems |
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