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In this work, we propose an approach for collecting completion usage logsfrom the users in an IDE and using them to train a machine learning based modelfor ranking completion candidates. We developed a set of features that describecompletion candidates and their context, and deployed their anonymizedcollection in the Early Access Program of IntelliJ-based IDEs. We used the logsto collect a dataset of code completions from users, and employed it to train aranking CatBoost model. Then, we evaluated it in two settings: on a held-outset of the collected completions and in a separate A/B test on two differentgroups of users in the IDE. Our evaluation shows that using a simple rankingmodel trained on the past user behavior logs significantly improved codecompletion experience. Compared to the default heuristics-based ranking, ourmodel demonstrated a decrease in the number of typing actions necessary toperform the completion in the IDE from 2.073 to 1.832.  The approach adheres to privacy requirements and legal constraints, since itdoes not require collecting personal information, performing all the necessaryanonymization on the client's side. Importantly, it can be improvedcontinuously: implementing new features, collecting new data, and evaluatingnew models - this way, we have been using it in production since the end of2020.

All You Need Is Logs: Improving Code Completion by Learning from Anonymous IDE Usage Logs