Achieving Productivity Gains with AI-based IDE features: A Journey at Google
Maxim Tabachnyk, Xu Shu, Alexander Fr\"ommgen, Pavel Sychev, Vahid Meimand, Ilia Krets, Stanislav Pyatykh, Abner Araujo, Krist\'of Moln\'ar, Satish Chandra

TL;DR
This paper details Google's development of AI-powered IDE features like code completion and natural-language code transformation, highlighting challenges, solutions, and productivity improvements in enterprise software development.
Contribution
It introduces novel AI-based IDE features and demonstrates effective strategies for integrating and refining these tools to enhance developer productivity.
Findings
Improved code suggestion accuracy and latency
Enhanced user experience with AI-driven code transformations
Quantifiable productivity gains in enterprise environments
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.
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Taxonomy
TopicsSoftware Engineering Research · Scientific Computing and Data Management · Ethics and Social Impacts of AI
