Abstract
In this study, a multivocal literature review identified 15 software-engineering design patterns for machine learning applications. Findings suggest that there are opportunities to increase the patterns’ adoption in practice by raising awareness of such patterns within the community.
Highlights
The popularity of machine learning (ML) techniques hasincreasedinrecentyears.MLis used in many domains, including cybersecurity, the Internet of Things, and autonomous cars
ML techniques rely on mathematics and software engineering
We describe one major ML design pattern to show how the ML design patterns are documented and used for resolving design problems
Summary
The popularity of machine learning (ML) techniques hasincreasedinrecentyears.MLis used in many domains, including cybersecurity, the Internet of Things, and autonomous cars. Researchers and practitioners study best practices to design ML application systems and software to address issues with software complexity and the quality of ML techniques. Such practices are often formalized as design patterns. Documents written in English addressing concrete software-engineering patterns or practices to design ML application systems and software should be included. One of the authors checked each pattern by reading the entire document to determine whether the pattern pertained to software-engineering design practices for ML systems. One of the authors analyzed the quality attributes by reading problems and solutions descriptions of the 15 ML design patterns and identifying related specific descriptions or keywords
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