research-article

Applying graph kernels to model-driven engineering problems

Authors:

Robert Clarisó,

Jordi CabotAuthors Info & Claims

MASES 2018: Proceedings of the 1st International Workshop on Machine Learning and Software Engineering in Symbiosis

Pages 1 - 5

https://doi.org/10.1145/3243127.3243128

Published: 03 September 2018 Publication History

Abstract

Machine Learning (ML) can be used to analyze and classify large collections of graph-based information, e.g. images, location information, the structure of molecules and proteins, ... Graph kernels is one of the ML techniques typically used for such tasks.

In a software engineering context, models of a system such as structural or architectural diagrams can be viewed as labeled graphs. Thus, in this paper we propose to employ graph kernels for clustering software modeling artifacts. Among other benefits, this would improve the efficiency and usability of a variety of software modeling activities, e.g., design space exploration, testing or verification and validation.

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Cited By

Khalilipour AChallenger MCombemale BWimmer MChechik MEgyed A(2024)Towards Intelligent Model Management: An Exploratory Study and Road-mappingProceedings of the ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems10.1145/3652620.3688224(1015-1024)Online publication date: 22-Sep-2024
https://dl.acm.org/doi/10.1145/3652620.3688224
Marcén AIglesias ALapeña RPérez FCetina C(2024)A Systematic Literature Review of Model-Driven Engineering Using Machine LearningIEEE Transactions on Software Engineering10.1109/TSE.2024.343051450:9(2269-2293)Online publication date: Sep-2024
https://doi.org/10.1109/TSE.2024.3430514
Khalilipour ABenito ADecraemer BDe Cock ATekin Tezel BBozyigit FChallenger M(2024)Model-driven Engineering of A Knowledge-base for Assembly Systems: An Architectural View2024 13th Mediterranean Conference on Embedded Computing (MECO)10.1109/MECO62516.2024.10577857(1-9)Online publication date: 11-Jun-2024
https://doi.org/10.1109/MECO62516.2024.10577857
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Index Terms

Applying graph kernels to model-driven engineering problems
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
    2. Machine learning approaches
      1. Kernel methods
2. Software and its engineering
  1. Software organization and properties
    1. Software system structures
      1. Software system models
        Model-driven software engineering

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cover image ACM Conferences

MASES 2018: Proceedings of the 1st International Workshop on Machine Learning and Software Engineering in Symbiosis

September 2018

52 pages

ISBN:9781450359726

DOI:10.1145/3243127

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGAI: ACM Special Interest Group on Artificial Intelligence
CNRS: Centre National De La Rechercue Scientifique
SIGSOFT: ACM Special Interest Group on Software Engineering
IEEE-CS: Computer Society

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 September 2018

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Spanish Ministry of Economy and Competitivity
H2020 ECSEL Joint Undertaking

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ASE '18

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SIGAI
CNRS
SIGSOFT
IEEE-CS

ASE '18: 33rd ACM/IEEE International Conference on Automated Software Engineering

September 3, 2018

Montpellier, France

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Cited By

Khalilipour AChallenger MCombemale BWimmer MChechik MEgyed A(2024)Towards Intelligent Model Management: An Exploratory Study and Road-mappingProceedings of the ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems10.1145/3652620.3688224(1015-1024)Online publication date: 22-Sep-2024
https://dl.acm.org/doi/10.1145/3652620.3688224
Marcén AIglesias ALapeña RPérez FCetina C(2024)A Systematic Literature Review of Model-Driven Engineering Using Machine LearningIEEE Transactions on Software Engineering10.1109/TSE.2024.343051450:9(2269-2293)Online publication date: Sep-2024
https://doi.org/10.1109/TSE.2024.3430514
Khalilipour ABenito ADecraemer BDe Cock ATekin Tezel BBozyigit FChallenger M(2024)Model-driven Engineering of A Knowledge-base for Assembly Systems: An Architectural View2024 13th Mediterranean Conference on Embedded Computing (MECO)10.1109/MECO62516.2024.10577857(1-9)Online publication date: 11-Jun-2024
https://doi.org/10.1109/MECO62516.2024.10577857
Khalilipour ABozyigit FChallenger M(2024)Intelligent Model Management based on Textual and Structural Extraction-An Exploratory Study2024 10th International Conference on Web Research (ICWR)10.1109/ICWR61162.2024.10533323(165-174)Online publication date: 24-Apr-2024
https://doi.org/10.1109/ICWR61162.2024.10533323
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
Ali SGavric AProper HBork D(2023)Encoding Conceptual Models for Machine Learning: A Systematic Review2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C)10.1109/MODELS-C59198.2023.00094(562-570)Online publication date: 1-Oct-2023
https://doi.org/10.1109/MODELS-C59198.2023.00094
Meyer WOosthuizen R(2023)Verification & Validation Methods for Complex AI-enabled Cyber-Physical Learning-Based Systems: A Systematic Literature Review2023 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC)10.1109/ICE/ITMC58018.2023.10332308(1-7)Online publication date: 19-Jun-2023
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Kasaei MSharbaf MFatemi AZamani B(2023)TrackMine: Topic Tracking in Model Mining using Genetic Algorithm2023 13th International Conference on Computer and Knowledge Engineering (ICCKE)10.1109/ICCKE60553.2023.10326247(271-276)Online publication date: 1-Nov-2023
https://doi.org/10.1109/ICCKE60553.2023.10326247
Di Sipio CDi Rocco JDi Ruscio DNguyen P(2023)MORGAN: a modeling recommender system based on graph kernelSoftware and Systems Modeling10.1007/s10270-023-01102-822:5(1427-1449)Online publication date: 4-Apr-2023
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Ruscio DNguyen PPierantonio A(2023)Machine Learning for Managing Modeling Ecosystems: Techniques, Applications, and a Research VisionSoftware Ecosystems10.1007/978-3-031-36060-2_10(249-279)Online publication date: 26-May-2023
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