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

TL;DR

This paper presents a privacy-compliant method for improving code completion in IDEs by learning from anonymized user logs, resulting in more efficient suggestions and fewer keystrokes needed.

Contribution

It introduces a novel approach to collect and utilize anonymized IDE usage logs for training a machine learning ranking model to enhance code completion.

Findings

Significant reduction in typing actions for code completion

Effective ranking model trained on user logs improves suggestions

Method complies with privacy and legal standards

Abstract

In this work, we propose an approach for collecting completion usage logs from the users in an IDE and using them to train a machine learning based model for ranking completion candidates. We developed a set of features that describe completion candidates and their context, and deployed their anonymized collection in the Early Access Program of IntelliJ-based IDEs. We used the logs to collect a dataset of code completions from users, and employed it to train a ranking CatBoost model. Then, we evaluated it in two settings: on a held-out set of the collected completions and in a separate A/B test on two different groups of users in the IDE. Our evaluation shows that using a simple ranking model trained on the past user behavior logs significantly improved code completion experience. Compared to the default heuristics-based ranking, our model demonstrated a decrease in the number of typing actions necessary to perform the completion in the IDE from 2.073 to 1.832. The approach adheres to privacy requirements and legal constraints, since it does not require collecting personal information, performing all the necessary anonymization on the client's side. Importantly, it can be improved continuously: implementing new features, collecting new data, and evaluating new models - this way, we have been using it in production since the end of 2020.