AI Tools for Software Development (CMU Course) (ai-developer-tools.github.io)

Carnegie Mellon launched 17-316/616 “AI Tools for Software Development,” a hands-on course that teaches students to apply AI-based developer tools across the software development lifecycle—coding, code review, requirements and spec writing, project management, automated testing, deployment, and security. The class requires substantial practice both with and without AI assistance and uses those experiences to drive an empirical analysis of how AI tools affect software productivity at the individual, team, and organizational levels. Weekly labs, reflections, and milestones scaffold practical work; projects run from requirements engineering and front-end/back-end development to testing (including synthetic and real-user testing), deployment, and a culminating presentation/postmortem. For the AI/ML community, the course is significant because it couples practical tool use with systematic measurement of outcomes, producing actionable insights about human–AI collaboration in software engineering. Key technical elements include “vibe coding” co-creation sessions (informed by prior qualitative work), iterative project checkpoints (P1–P8), testing specifications, and deployment labs—plus guest lectures and office hours for mentorship. The curriculum’s emphasis on side-by-side comparisons (with/without AI) and real-world project evaluation should generate empirical evidence valuable to tool designers, researchers studying developer productivity, and educators integrating AI into software engineering curricula.