Interactive metamodel/model co-evolution using unsupervised learning and multi-objective search-Reference-Cited by-同舟云学术

Interactive metamodel/model co-evolution using unsupervised learning and multi-objective search

Published:2020-10-16 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems
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Author:

Kessentini Wael¹,Alizadeh Vahid¹

Affiliation:

1. DePaul University

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3365438.3410966

Reference44 articles.

1. [n.d.]. Constraints for model co-evolution: https://docs.google.eom/document/d/1O1GjOcvvBgPuVacB12noygNaJ75y0nwySdGmiqGXouQ. [n.d.]. Constraints for model co-evolution: https://docs.google.eom/document/d/1O1GjOcvvBgPuVacB12noygNaJ75y0nwySdGmiqGXouQ.

2. Hybrid Approach for Metamodel and Model Co-evolution

3. A practical guide for using statistical tests to assess randomized algorithms in software engineering

4. Marco Brambilla Jordi Cabot and Manuel Wimmer. 2017. Model-Driven Software Engineering in Practice. Morgan & Claypool Publishers. Marco Brambilla Jordi Cabot and Manuel Wimmer. 2017. Model-Driven Software Engineering in Practice. Morgan & Claypool Publishers.

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