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Finding with NEMO: a recommender system to forecast the next modeling operations

Authors:

Claudio Di Sipio,

Phuong T. Nguyen,

Davide Di Ruscio,

Alfonso PierantonioAuthors Info & Claims

MODELS '22: Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems

Pages 154 - 164

https://doi.org/10.1145/3550355.3552459

Published: 24 October 2022 Publication History

Abstract

Nowadays, while modeling environments provide users with facilities to specify different kinds of artifacts, e.g., metamodels, models, and transformations, the possibility of learning from previous modeling experiences and being assisted during modeling tasks remains largely unexplored. In this paper, we propose NEMO, a recommender system based on an Encoder-Decoder neural network to assist modelers in performing model editing operations. NEMO learns from past modeling activities and performs predictions employing a deep learning technique. Such an algorithm has been successfully applied in machine translation to convert a text from a language to another foreign language and vice versa. An empirical evaluation on a dataset of BPMN change-based persistent model demonstrates that the technique permits learning from existing operations and effectively predicting the next editing operations with considerably high prediction accuracy. In particular, NEMO gets 0.977 as precision/recall and 0.992 as success rate score by the best performance.

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Cited By

Muttillo VDi Sipio CRubei RBerardinelli LDehghani MFilkov VRay BZhou M(2024)Towards Synthetic Trace Generation of Modeling Operations using In-Context Learning ApproachProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695058(619-630)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695058
Cederbladh JBerardinelli LBruneliere HCicchetti ADehghani MDi Sipio CMiranda JRahimi ARubei RSuryadevara J(2024)Towards Automating Model-Based Systems Engineering in Industry - An Experience Report2024 IEEE International Systems Conference (SysCon)10.1109/SysCon61195.2024.10553610(1-8)Online publication date: 15-Apr-2024
https://doi.org/10.1109/SysCon61195.2024.10553610
Liu HDong YKe QZhou Z(2024)ReCo: A Modular Neural Framework for Automatically Recommending Connections in Software Models2024 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)10.1109/SANER60148.2024.00070(637-648)Online publication date: 12-Mar-2024
https://doi.org/10.1109/SANER60148.2024.00070
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cover image ACM Conferences

MODELS '22: Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems

October 2022

412 pages

ISBN:9781450394666

DOI:10.1145/3550355

General Chairs:
Eugene Syriani
Université de Montréal, Canada
,
Houari Sahraoui
Université de Montréal, Canada

Copyright © 2022 ACM.

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Published: 24 October 2022

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MODELS '22: ACM/IEEE 25th International Conference on Model Driven Engineering Languages and Systems

October 23 - 28, 2022

Quebec, Montreal, Canada

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MODELS '22 Paper Acceptance Rate 35 of 125 submissions, 28%;

Overall Acceptance Rate 144 of 506 submissions, 28%

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Cited By

Muttillo VDi Sipio CRubei RBerardinelli LDehghani MFilkov VRay BZhou M(2024)Towards Synthetic Trace Generation of Modeling Operations using In-Context Learning ApproachProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695058(619-630)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695058
Cederbladh JBerardinelli LBruneliere HCicchetti ADehghani MDi Sipio CMiranda JRahimi ARubei RSuryadevara J(2024)Towards Automating Model-Based Systems Engineering in Industry - An Experience Report2024 IEEE International Systems Conference (SysCon)10.1109/SysCon61195.2024.10553610(1-8)Online publication date: 15-Apr-2024
https://doi.org/10.1109/SysCon61195.2024.10553610
Liu HDong YKe QZhou Z(2024)ReCo: A Modular Neural Framework for Automatically Recommending Connections in Software Models2024 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)10.1109/SANER60148.2024.00070(637-648)Online publication date: 12-Mar-2024
https://doi.org/10.1109/SANER60148.2024.00070
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://dl.acm.org/doi/10.1007/s10664-024-10483-3
López JDurá CCuadrado J(2024)Experimenting with modeling-specific word embeddingsSoftware and Systems Modeling10.1007/s10270-024-01250-5Online publication date: 12-Dec-2024
https://doi.org/10.1007/s10270-024-01250-5
Gomes AMaia P(2023)DoME: An Architecture for Domain Model Evolution at Runtime Using NLPProceedings of the XXXVII Brazilian Symposium on Software Engineering10.1145/3613372.3613405(186-195)Online publication date: 25-Sep-2023
https://dl.acm.org/doi/10.1145/3613372.3613405
Hernández López JDurá CCuadrado J(2023)Word Embeddings for Model-Driven Engineering2023 ACM/IEEE 26th International Conference on Model Driven Engineering Languages and Systems (MODELS)10.1109/MODELS58315.2023.00036(151-161)Online publication date: 1-Oct-2023
https://doi.org/10.1109/MODELS58315.2023.00036

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