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

Maxim Tabachnyk; Xu Shu; Alexander Frömmgen; Sychev, Pavel; Vahid Meimand; Ilia Krets; Stanislav Pyatykh; Abner M. C. Araújo; Kristóf Molnár; Satish Chandra

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

arXiv (Cornell University) 2026 Preprint Open access

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Authors: Maxim Tabachnyk, Xu Shu, Alexander Frömmgen, Sychev, Pavel, Vahid Meimand, Ilia Krets, Stanislav Pyatykh, Abner M. C. Araújo, Kristóf Molnár, Satish Chandra

Cited by: 0

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.

Fields of Study & Subjects

Software Engineering Research Software Engineering Techniques and Practices Information Retrieval and Search Behavior

Publication details

Journal / source: arXiv (Cornell University)
Publication date: 27 January 2026
Document type: Preprint