Teaching Software Engineering for Al-Enabled Systems
Teaching Software Engineering for Al-Enabled Systems
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DOI：
10.1145/3377814.3381714
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发表时间：
2020-01
期刊：
影响因子：
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通讯作者：
Christian Kästner;Eunsuk Kang
中科院分区：
文献类型：
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作者：
Christian Kästner;Eunsuk Kang
软件工程师在构建智能系统时可以提供重要的专业知识，利用数十年的经验和方法来构建可扩展，响应和强大的系统，即使是在不可靠的组件上构建。具有人工智能或机器学习（ML）组件的系统提出了新的挑战，需要精心设计。我们设计了一门新课程，向具有ML背景的学生教授软件工程技能。我们特别超越了在人工条件下教授建模技术的传统ML课程，并在讲座和作业中专注于大型和不断变化的数据集，强大和可进化的基础设施以及考虑道德和公平的有目的的需求工程。我们描述了课程和我们的基础设施，并分享经验和第一次教授课程的所有材料。
Software engineers have significant expertise to offer when building intelligent systems, drawing on decades of experience and methods for building systems that are scalable, responsive and robust, even when built on unreliable components. Systems with artificial-intelligence or machine-learning (ML) components raise new challenges and require careful engineering. We designed a new course to teach software-engineering skills to students with a background in ML. We specifically go beyond traditional ML courses that teach modeling techniques under artificial conditions and focus, in lecture and assignments, on realism with large and changing datasets, robust and evolvable infrastructure, and purposeful requirements engineering that considers ethics and fairness as well. We describe the course and our infrastructure and share experience and all material from teaching the course for the first time.