Hironori Washizaki, Hiromu Uchida, Foutse Khomh and Yann-Gaël Guéhéneuc, “Studying Software Engineering Patterns for Designing Machine Learning Systems,” The 10th International Workshop on Empirical Software Engineering in Practice (IWESEP 2019), Tokyo, Japan, on December 13-14, 2019.

Machine-learning (ML) techniques have become popular in the recent years. ML techniques rely on mathematics and on software engineering. Researchers and practitioners studying best practices for designing ML application systems and software to address the software complexity and quality of ML techniques. Such design practices are often formalized as architecture patterns and design patterns by encapsulating reusable solutions to commonly occurring problems within given contexts. However, to the best of our knowledge, there has been no work collecting, classifying, and discussing these software-engineering (SE) design patterns for ML techniques systematically. Thus, we set out to collect good/bad SE design patterns for ML techniques to provide developers with a comprehensive and ordered classification of such patterns. We report here preliminary results of a systematic-literature review (SLR) of good/bad design patterns for ML.