2019/10/10 by Hironori Washizaki, Washizaki, Hironori, Hiromu Uchida +5 · 1 citation
Business, Management and Accounting · Computer Science · #Big Data and Business Intelligence #FOS: Computer and information sciences #Machine Learning (cs.LG) #Software Engineering (cs.SE) #Software Engineering Research #Software System Performance and Reliability
paper · pdf · doi:10.48550/arxiv.1910.04736
openalex publication_date 2019/10/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Machine-learning (ML) techniques have become popular in the recent years. ML\ntechniques rely on mathematics and on software engineering. Researchers and\npractitioners studying best practices for designing ML application systems and\nsoftware to address the software complexity and quality of ML techniques. Such\ndesign practices are often formalized as architecture patterns and design\npatterns by encapsulating reusable solutions to commonly occurring problems\nwithin given contexts. However, to the best of our knowledge, there has been no\nwork collecting, classifying, and discussing these software-engineering (SE)\ndesign patterns for ML techniques systematically. Thus, we set out to collect\ngood/bad SE design patterns for ML techniques to provide developers with a\ncomprehensive and ordered classification of such patterns. We report here\npreliminary results of a systematic-literature review (SLR) of good/bad design\npatterns for ML.\n