research-article

Towards automating the construction of recommender systems for low-code development platforms

Authors:

Lissette Almonte,

Iván Cantador,

Juan de LaraAuthors Info & Claims

MODELS '20: Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings

Article No.: 66, Pages 1 - 10

https://doi.org/10.1145/3417990.3420200

Published: 26 October 2020 Publication History

Abstract

Low-code development platforms allow users with a low technical background to build complete software solutions, typically by means of graphical user interfaces, diagrams or declarative languages. In these platforms, recommender systems play an important role as they can provide users with relevant, personalised suggestions generated according to previously developed software solutions. However, developing recommender systems requires a high investment of time as it implies the selection and implementation of a suitable recommendation method, its configuration for the problem and domain at hand, and its evaluation to assess the accuracy of its recommendations.

To alleviate these problems, in this paper, we present the first steps towards a generic model-driven framework capable of generating ad-hoc, task-oriented recommender systems for their integration on low-code platforms. As a proof of concept, we present some preliminary results obtained from an offline evaluation of our framework on three datasets of class diagrams. The results show that the proposed framework is capable of providing relevant recommendations in the given context.

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Guresci ETekinerdogan BBabur ÖLiu Q(2024)Feasibility of Low-Code Development Platforms in Precision Agriculture: Opportunities, Challenges, and Future DirectionsLand10.3390/land1311175813:11(1758)Online publication date: 25-Oct-2024
https://doi.org/10.3390/land13111758
Liu DJiang HGuo SChen YQiao L(2024)What's Wrong With Low-Code Development Platforms? An Empirical Study of Low-Code Development Platform BugsIEEE Transactions on Reliability10.1109/TR.2023.329500973:1(695-709)Online publication date: Mar-2024
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Almonte LGuerra ECantador Ide Lara J(2024)Engineering recommender systems for modelling languages: concept, tool and evaluationEmpirical Software Engineering10.1007/s10664-024-10483-329:4Online publication date: 18-Jun-2024
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Index Terms

Towards automating the construction of recommender systems for low-code development platforms
1. Software and its engineering
  1. Software notations and tools
    1. Development frameworks and environments

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cover image ACM Conferences

MODELS '20: Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings

October 2020

713 pages

ISBN:9781450381352

DOI:10.1145/3417990

Conference Chairs:
Esther Guerra
Universidad Autonoma de Madrid, Spain
,
Ludovico Iovino
Gran Sasso Science Institute, Italy

Copyright © 2020 ACM.

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Publication History

Published: 26 October 2020

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Funding Sources

EU Horizon 2020 research and innovation programme under the Marie Sk?odowska- Curie
R&D programme of Madrid
Spanish Ministry of Science

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MODELS '20

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SIGSOFT

MODELS '20: ACM/IEEE 23rd International Conference on Model Driven Engineering Languages and Systems

October 16 - 23, 2020

Virtual Event, Canada

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Overall Acceptance Rate 144 of 506 submissions, 28%

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Cited By

Guresci ETekinerdogan BBabur ÖLiu Q(2024)Feasibility of Low-Code Development Platforms in Precision Agriculture: Opportunities, Challenges, and Future DirectionsLand10.3390/land1311175813:11(1758)Online publication date: 25-Oct-2024
https://doi.org/10.3390/land13111758
Liu DJiang HGuo SChen YQiao L(2024)What's Wrong With Low-Code Development Platforms? An Empirical Study of Low-Code Development Platform BugsIEEE Transactions on Reliability10.1109/TR.2023.329500973:1(695-709)Online publication date: Mar-2024
https://doi.org/10.1109/TR.2023.3295009
Almonte LGuerra ECantador Ide Lara J(2024)Engineering recommender systems for modelling languages: concept, tool and evaluationEmpirical Software Engineering10.1007/s10664-024-10483-329:4Online publication date: 18-Jun-2024
https://doi.org/10.1007/s10664-024-10483-3
López JCuadrado JRubei RDi Ruscio D(2024)ModelXGlue: a benchmarking framework for ML tools in MDESoftware and Systems Modeling10.1007/s10270-024-01183-zOnline publication date: 10-Jun-2024
https://doi.org/10.1007/s10270-024-01183-z
Oakes BFamelis MSahraoui H(2023)Building Domain-Specific Machine Learning Workflows: A Conceptual Framework for the State of the PracticeACM Transactions on Software Engineering and Methodology10.1145/363824333:4(1-50)Online publication date: 21-Dec-2023
https://dl.acm.org/doi/10.1145/3638243
Rosa-Bilbao JBoubeta-Puig JRutle A(2023)EDALoCoComputer Standards & Interfaces10.1016/j.csi.2022.10367684:COnline publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.csi.2022.103676
Bucaioni ACicchetti ACiccozzi F(2022)Modelling in low-code development: a multi-vocal systematic reviewSoftware and Systems Modeling10.1007/s10270-021-00964-021:5(1959-1981)Online publication date: 19-Jan-2022
https://doi.org/10.1007/s10270-021-00964-0
Rokis KKirikova M(2022)Challenges of Low-Code/No-Code Software Development: A Literature ReviewPerspectives in Business Informatics Research10.1007/978-3-031-16947-2_1(3-17)Online publication date: 16-Sep-2022
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Almonte LPérez-Soler SGuerra ECantador Ide Lara JVisser EKolovos DSöderberg E(2021)Automating the synthesis of recommender systems for modelling languagesProceedings of the 14th ACM SIGPLAN International Conference on Software Language Engineering10.1145/3486608.3486905(22-35)Online publication date: 17-Oct-2021
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Di Sipio CDi Rocco JDi Ruscio DNguyen D(2021)A Low-Code Tool Supporting the Development of Recommender SystemsProceedings of the 15th ACM Conference on Recommender Systems10.1145/3460231.3478885(741-744)Online publication date: 13-Sep-2021
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