AI-Native Software Engineering: Enduring Principles, New Pace

A private, global research university, Carnegie Mellon stands among the world's most renowned educational institutions, and sets its own course.Start the journey here.With cutting-edge brain science, path-breaking performances, innovative start-ups, driverless cars, big data, big ambitions, Nobel and Turing prizes, hands-on learning, and a whole lot of robots, CMU doesn't imagine the future, we create it.

Carnegie Mellon University

SEI researchers discuss observations from their application of AI-native SW engineering and the study of its use across multiple industries.

AI is rapidly changing how software is produced but not the need to engineer software to meet business and mission goals. AI is enabling developers to move from idea to implementation at incredible speeds. However, this fast pace has implications that teams must manage. Product quality does not come for free, and there is some tendency for AI to accelerate the accumulation of technical debt. In addition, what works well on small code bases doesn't always work as well on large code bases. So, what's a good software engineer to do?Software engineering principles and practices are essential in guiding software engineers to use AI to achieve production-ready outcomes. In this webcast, experienced software engineers discuss their observations and lessons from applying AI-native software engineering and studying its use across multiple projects.What Will Attendees Learn?<ul><li>Distinguish between “vibe coding” and software engineering</li><li>Understand how software engineering principles improve the use of AI and where these principles need to be adapted to be used with AI</li><li>Recognize the different criteria against which to assess the benefits of AI-native software engineering (e.g., productivity and software quality) and their potential tradeoffs</li></ul>Who Should Attend?<ul><li>AI Developers, vibe coders, and software engineers at all experience levels</li><li>Architects, business analysts, quality assurance engineers</li><li>Technical leads and engineering managers</li></ul>About the SpeakersScott Sinclair is a Software Architect at the Software Engineering Institute (SEI) with over 20 years of experience across all aspects of the software design lifecycle. He supports multiple organizations by advancing architecture practices and delivering practical solutions. He also teaches SEI courses on software architecture design, analysis, and documentation. His current work includes modernizing legacy software using AI alongside traditional modernization activities.James Ivers is a Principal Engineer and lead of the AI Workflows and Architecture Modernization group at the Software Engineering Institute (SEI). His experience spans 30 years of research and application of work in software architecture, code analysis, formal methods, and scaling the ability to evolve software. He is a coauthor of the book Documenting Software Architectures. His most recent work focuses on using AI for software engineering to support large-scale software modernization.Mario Benitez is a Software Architect at the Software Engineering Institute, where he works across organizations, from architecting large-scale systems to advancing software architecture practices. With over 20 years of experience in software engineering, he has built and delivered complex, high-reliability systems, including those in safety-critical environments. He currently focuses on the practical application of AI to enhance large-scale software modernization and enable organizations to transform complex systems more efficiently and effectively.

Rethinking and Maturing AI Adoption

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AI-Native Software Engineering: Enduring Principles, New Pace

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AI-Native Software Engineering: Enduring Principles, New Pace

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Rethinking and Maturing AI Adoption

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