Machine Learning-Based Software Development challenges focusing on using best practices of Software Engineering Standards

被引:0
作者
Tablada-Dominguez, Antonio [1 ]
Munoz, Mirna [1 ]
Ocharan-Hernandez, Jorge Octavio [2 ]
Sanchez-Garcia, Angel J. [2 ]
机构
[1] Ctr Inmvestigac Matemat AC, Zacatecas, Zacatecas, Mexico
[2] Univ Veracruzana, Xalapa, Veracruz, Mexico
来源
2023 MEXICAN INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE, ENC | 2024年
关键词
Software engineering; software development; software processes; software best practices; machine learning; artificial intelligence;
D O I
10.1109/ENC60556.2023.10508685
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The AI era established significant challenges for software developers, especially those working on Machine Learning (ML)-based software. This article presents the findings of a systematic literature review (SLR) focused on identifying software engineering practices for ML-based software development. We identified 16 primary studies highlighting the challenges scientists face in lacking software engineering training when developing ML-based software. The results emphasize the importance of documentation, standardized processes, and skills acquisition to overcome these challenges effectively in the AI era.
引用
收藏
页数:7
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