Teaching Software Engineering for AI-Enabled Systems

• URL: http://arxiv.org/abs/2001.06691v1
• Date: Sat, 18 Jan 2020 15:24:17 GMT
• Title: Teaching Software Engineering for AI-Enabled Systems
• Abstract summary: This course teaches software-engineering skills to students with a background in machine learning. We describe the course and our infrastructure and share experience and all material from teaching the course for the first time.
• Score: 7.01053472751897
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Giving Mechanical Engineers Intelligent Tools: A Project-Based AI Education Curriculum in Thermal Engineering [7.034388558444647]
  This paper proposes a new curriculum that integrates artificial intelligence (AI) into mechanical engineering (ME) at the University of Arkansas (UARK)<n>The curriculum has introductory, application, and advanced levels, covering core and optional AI projects.<n>Key goals are to enhance students' understanding of AI models, ability to tackle engineering tasks, and teach multidisciplinary communication skills.
  arXiv  Detail & Related papers  (2026-08-26T17:26:22Z)
• Beyond Models: Reflections on Engineering AI-enabled Systems in a Project-Based Course [6.295638112781735]
  This paper reflects on the design and implementation of a project-based master's-level course titled AI Algorithms: Theory and Engineering, at the University of Bremen.<n>Students developed a movie recommendation system while making architectural design decisions to address challenges related to scalability, deployment, and evolving requirements.<n>Our results indicate persistent difficulties in early architectural decisions, heterogeneous ML integration, evolving requirements, and data management, largely due to uneven ML and software engineering expertise.
  arXiv  Detail & Related papers  (2026-06-15T15:21:54Z)
• Teaching Machine Learning to Software Engineers [1.217503190366097]
  Machine learning (ML) and Artificial Intelligence (AI) components are increasingly embedded in software products.<n>Software engineering curricula rarely provide systematic preparation for building, testing, deploying, and maintaining AI/ML-based software systems.<n>This paper aims to provide evidence-based guidance for integrating AI/MLrelevant content into core SE education.
  arXiv  Detail & Related papers  (2026-06-12T16:42:34Z)
• Reshaping Undergraduate Computer Science Education in the Generative AI Era [85.78707817763434]
  Generative AI represents a turning point for Computer Science (CS) education.<n>To keep pace with advances in AI technology, CS curricula should consider a shift toward understanding and verifying AI-generated artifacts.<n>We believe that to effectively prepare future computer science graduates, capable of creating, solving problems, and managing, as well as co-creating, artifacts with AI.
  arXiv  Detail & Related papers  (2026-05-02T05:26:24Z)
• Beyond the Classroom: Bridging the Gap Between Academia and Industry with a Hands-on Learning Approach [2.8123958518740544]
  Self-adaptive software systems have emerged as a critical focus in software design and operation. A survey among practitioners identified that the lack of knowledgeable individuals has hindered its adoption in the industry. We present our experience teaching a course on self-adaptive software systems that integrates theoretical knowledge and hands-on learning with industry-relevant technologies.
  arXiv  Detail & Related papers  (2025-04-14T21:32:25Z)
• A quantitative framework for evaluating architectural patterns in ML systems [49.1574468325115]
  This study proposes a framework for quantitative assessment of architectural patterns in ML systems. We focus on scalability and performance metrics for cost-effective CPU-based inference.
  arXiv  Detail & Related papers  (2025-01-20T15:30:09Z)
• Machine Learning-Enabled Software and System Architecture Frameworks [48.87872564630711]
  The stakeholders with data science and Machine Learning related concerns, such as data scientists and data engineers, are yet to be included in existing architecture frameworks. We surveyed 61 subject matter experts from over 25 organizations in 10 countries.
  arXiv  Detail & Related papers  (2023-08-09T21:54:34Z)
• Utilizing Online and Open-Source Machine Learning Toolkits to Leverage the Future of Sustainable Engineering [8.641860292533023]
  Edge Impulse has designed an open-source TinyML-enabled Arduino education tool kit for engineering disciplines. This paper discusses the various applications and approaches engineering educators have taken to utilize ML toolkits in the classroom.
  arXiv  Detail & Related papers  (2023-04-21T17:50:21Z)
• Enabling Automated Machine Learning for Model-Driven AI Engineering [60.09869520679979]
  We propose a novel approach to enable Model-Driven Software Engineering and Model-Driven AI Engineering. In particular, we support Automated ML, thus assisting software engineers without deep AI knowledge in developing AI-intensive systems.
  arXiv  Detail & Related papers  (2022-03-06T10:12:56Z)
• Panoramic Learning with A Standardized Machine Learning Formalism [116.34627789412102]
  This paper presents a standardized equation of the learning objective, that offers a unifying understanding of diverse ML algorithms. It also provides guidance for mechanic design of new ML solutions, and serves as a promising vehicle towards panoramic learning with all experiences.
  arXiv  Detail & Related papers  (2021-08-17T17:44:38Z)
• Technology Readiness Levels for Machine Learning Systems [107.56979560568232]
  Development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. We have developed a proven systems engineering approach for machine learning development and deployment. Our "Machine Learning Technology Readiness Levels" framework defines a principled process to ensure robust, reliable, and responsible systems.
  arXiv  Detail & Related papers  (2021-01-11T15:54:48Z)
• Using game simulator Software Inc in the Software Engineering education [0.0]
  The article presents the possibilities of using game simulator Sotware Inc in the training of future software engineers. The use of modern ICT, including game simulators, in the educational process allows to improve the quality of educational material.
  arXiv  Detail & Related papers  (2020-11-26T13:26:55Z)
• Technology Readiness Levels for AI & ML [79.22051549519989]
  Development of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. Engineering systems follow well-defined processes and testing standards to streamline development for high-quality, reliable results. We propose a proven systems engineering approach for machine learning development and deployment.
  arXiv  Detail & Related papers  (2020-06-21T17:14:34Z)
• Machine Learning for Software Engineering: A Systematic Mapping [73.30245214374027]
  The software development industry is rapidly adopting machine learning for transitioning modern day software systems towards highly intelligent and self-learning systems. No comprehensive study exists that explores the current state-of-the-art on the adoption of machine learning across software engineering life cycle stages. This study introduces a machine learning for software engineering (MLSE) taxonomy classifying the state-of-the-art machine learning techniques according to their applicability to various software engineering life cycle stages.
  arXiv  Detail & Related papers  (2020-05-27T11:56:56Z)
• Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology [53.063411515511056]
  We propose a process model for the development of machine learning applications. The first phase combines business and data understanding as data availability oftentimes affects the feasibility of the project. The sixth phase covers state-of-the-art approaches for monitoring and maintenance of a machine learning applications.
  arXiv  Detail & Related papers  (2020-03-11T08:25:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.