Software Engineering Patterns for Machine Learning Application Systems

Milhem, Hind; Harrison, Neil; Washizaki, Hironori; Uchida, Hiromu; Khomh, Foutse; Guhneuc, Yann-Gal; Kla, Michael; Vollmer, Anna; Yokoyama, Haruki; Harrison, Neil; Meszaros, Gerhard; Doble, Jim; Wellhausen, Tim; Fiesser, Andreas; Chouliaras, Georgios; Kieczewski; Kornel; Beka; Amit; Konopnicki, David; Bernardi, Lucas; Roy, Swarup; Sharma; Pooja; Nath; Keshab; Bhattacharyya, Dhruba; Kalita; Salehin, Md Imrus; Islam, Pritom; Saha, S; Noman, Azra; Tuni, Md; Hasan, Md Mehedi; Baten; Ali, Sajid; Abuhmed, Tamer; El-Sappagh, Shaker; Muhammad, Khan; Alonso-Moral, Jose; Confalonieri, Roberto; Guidotti, Riccardo; Ser, Javier Del; Daz-Rodrguez, Natalia; Herrera, Francisco; Mumuni, Alhassan; Mumuni, Fuseini; Velepucha, Victor

doi:10.64346/plop2024p12

Public

Software Engineering Patterns for Machine Learning Application Systems

Published • Aug 24, 2025

NobleIDNI6P77W70R52S05

Authors:

Hind Milhem

,

Neil Harrison

,

Hironori Washizaki

Abstract

Software Engineering Patterns provide reusable solutions to common design problems, offering a structured framework for developing robust, scalable, and maintainable software systems.When applied to the realm of machine learning, these patterns offer invaluable guidance in navigating the complexitie...

Subject

Computer science

Software engineering

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