Machine learning-based incremental learning in interactive domain modelling
Author:
Affiliation:
1. McGill University, Montréal, QC, Canada
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3550355.3552421
Reference27 articles.
1. Extracting domain models from natural-language requirements
2. RaM: Causally-Connected and Requirements-Aware Runtime Models using Bayesian Learning
3. An NLP-Based Architecture for the Autocompletion of Partial Domain Models
4. An LSTM-Based Neural Network Architecture for Model Transformations
5. A Content-Based Approach for Recommending UML Sequence Diagrams
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1. Automated Domain Modeling with Large Language Models: A Comparative Study;2023 ACM/IEEE 26th International Conference on Model Driven Engineering Languages and Systems (MODELS);2023-10-01
2. DoME: An Architecture for Domain Model Evolution at Runtime Using NLP;Proceedings of the XXXVII Brazilian Symposium on Software Engineering;2023-09-25
3. Incrementally Mining Column Constant Biclusters with FVSFP Tree;Applied Sciences;2023-05-25
4. Automated, traceable, and interactive domain modelling;Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings;2022-10-23
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