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arXiv · 2601.20223

Control Models for In-IDE Code Completion

Aral de Moor·Yana Hrynevich·Hleb Badzeika·Vladyslav Furda·Marko Kojic·Artem Savelev·Kostadin Cvejoski·Darya Rovdo·Ekaterina Garanina
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Abstract

We introduce control models for LLM-powered code completion in JetBrains IDEs: ML classifiers which trigger inference and filter the generated suggestions to better align them with users and reduce unnecessary requests. To this end, we evaluate boosting- and transformer-based architectures on an offline dataset of real code completions with n=98 users. We further evaluate the offline classification performance of our boosting-based approach on a range of syntactically diverse languages; and perform an A/B study in a production environment where they improve completion efficiency and quality metrics. With this study, we hope to demonstrate the potential in using auxiliary models for smarter in-IDE integration of LLM-driven features, highlight fruitful future directions, and open problems.

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Aral de Moor, Yana Hrynevich, Hleb Badzeika, Vladyslav Furda, Marko Kojic, Artem Savelev, Kostadin Cvejoski, Darya Rovdo, Ekaterina Garanina. 2026-01-28. Control Models for In-IDE Code Completion. https://doi.org/10.1145/3786151.3788608

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