Achieving Productivity Gains with AI-based IDE features: A Journey at Google
🤖 AI Summary
This work proposes an end-to-end co-optimization framework to address the limitations of AI-powered programming assistants in enterprise development environments, particularly concerning latency, suggestion quality, and user experience. The framework jointly refines the user interface, backend systems, and underlying AI models—including code completion and natural language–to–code transformation components. By establishing a developer-centric iterative experimentation platform that integrates A/B testing and behavioral analytics, the approach was deployed at scale within Google. The deployment yielded significant improvements in code completion accuracy and adoption rates of the Transform Code feature, resulting in measurable gains in developer productivity.
Application Category
📝 Abstract
We discuss Google's journey in developing and refining two internal AI-based IDE features: code completion and natural-language-driven code transformation (Transform Code). We address challenges in latency, user experience and suggestion quality, all backed by rigorous experimentation. The article serves as an example of how to refine AI developer tools across the user interface, backend, and model layers, to deliver tangible productivity improvements in an enterprise setting.
Problem
Research questions and friction points this paper is trying to address.
AI-based IDE
productivity gains
code completion
natural-language-driven code transformation
developer tools
Innovation
Methods, ideas, or system contributions that make the work stand out.
AI-based IDE
code completion
natural-language-driven code transformation
productivity gains
developer tools
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