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arXiv:2601.19964cs.SE

Achieving Productivity Gains with AI-based IDE features: A Journey at Google

Maxim Tabachnyk, Xu Shu, Alexander Frömmgen, Pavel Sychev, Vahid Meimand, Ilia Krets, Stanislav Pyatykh, Abner Araujo, Kristóf Molnár, Satish Chandra

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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.

arXiv comments
Accepted for publication at the 3rd International Workshop on Large Language Models For Code (LLM4Code '26 workshop at ICSE '26)
Journal reference
DOI
Subjects
cs.SESoftware Engineering

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