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Integrated multi-view modeling for reliable machine learning-intensive software engineering.
- Source :
- Software Quality Journal; Sep2024, Vol. 32 Issue 3, p1239-1285, 47p
- Publication Year :
- 2024
-
Abstract
- Development of machine learning (ML) systems differs from traditional approaches. The probabilistic nature of ML leads to a more experimentative development approach, which often results in a disparity between the quality of ML models with other aspects such as business, safety, and the overall system architecture. Herein the Multi-view Modeling Framework for ML Systems (M<superscript>3</superscript>S) is proposed as a solution to this problem. M<superscript>3</superscript>S provides an analysis framework that integrates different views. It is supported by an integrated metamodel to ensure the connection and consistency between different models. To facilitate the experimentative nature of ML training, M<superscript>3</superscript>S provides an integrated platform between the modeling environment and the ML training pipeline. M<superscript>3</superscript>S is validated through a case study and a controlled experiment. M<superscript>3</superscript>S shows promise, but future research needs to confirm its generality. [ABSTRACT FROM AUTHOR]
- Subjects :
- MACHINE learning
SOFTWARE engineering
MACHINERY
Subjects
Details
- Language :
- English
- ISSN :
- 09639314
- Volume :
- 32
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- Software Quality Journal
- Publication Type :
- Academic Journal
- Accession number :
- 178560471
- Full Text :
- https://doi.org/10.1007/s11219-024-09687-z