Bringing Software Engineering Discipline to the Development of AI-Enabled Systems

被引：0

作者：

Staron, Miroslaw ^{[1
,2
]}

Abrahao, Silvia ^{[3
]}

Lewis, Grace ^{[4
]}

Muccini, Henry ^{[5
]}

Honnenahalli, Chetan ^{[6
]}

机构：

[1] Chalmers Univ Technol, Dept Comp Sci & Engn, Interact Design & Software Engn Div, S-41296 Gothenburg, Sweden

[2] Univ Gothenburg, S-41296 Gothenburg, Sweden

[3] Univ Politecn Valencia, Dept Comp Syst & Computat, Valencia 46022, Spain

[4] Carnegie Mellon Software Engn Inst SEI, Tact & AI Enabled Syst TAS Initiat, Pittsburgh, PA 15213 USA

[5] Univ Aquila, Software Engn, I-67100 Laquila, Italy

[6] Meta, Menlo Pk, CA 94025 USA

来源：

IEEE SOFTWARE | 2024年 / 41卷 / 05期

关键词：

Machine learning; Software systems; Modeling; Software engineering; Software development management;

D O I：

10.1109/MS.2024.3408388

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Engineering AI Software systems is starting to evolve from the pure development of machine learning (ML) models to a more structured discipline that treats ML components as part of much larger software systems. As such, more structured principles are required for their development, such as established design principles, established development models, and safeguards for deployed ML models. This column focuses on papers presented at the Third International Conference on AI Engineering-Software Engineering for AI (CAIN 2024). The selected papers reflect the current development of the field of AI systems engineering and AI software development, taking it to the next level of maturity. Feedback or suggestions are welcome. In addition, if you try or adopt any of the practices included in the column, please send us and the authors of the paper(s) a note about your experiences.

引用

页码：79 / 82

页数：4

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