TreeRanker: Fast and Model-agnostic Ranking System for Code Suggestions in IDEs

TL;DR

TreeRanker introduces a fast, model-agnostic ranking method for code suggestions in IDEs, leveraging prefix trees and token-level scoring to improve prediction relevance without complex decoding.

Contribution

It proposes a novel, lightweight, and architecture-agnostic ranking approach using prefix trees and greedy decoding for static code completions.

Findings

Enables precise token-aware ranking without beam search.

Compatible with existing language models and IDEs.

Improves relevance of code suggestions in IDEs.

Abstract

Token-level code completion is one of the most critical features in modern Integrated Development Environments (IDEs). It assists developers by suggesting relevant identifiers and APIs during coding. While completions are typically derived from static analysis, their usefulness depends heavily on how they are ranked, as correct predictions buried deep in the list are rarely seen by users. Most current systems rely on hand-crafted heuristics or lightweight machine learning models trained on user logs, which can be further improved to capture context information and generalize across projects and coding styles. In this work, we propose a new scoring approach to ranking static completions using language models in a lightweight and model-agnostic way. Our method organizes all valid completions into a prefix tree and performs a single greedy decoding pass to collect token-level scores across the tree. This enables a precise token-aware ranking without needing beam search, prompt engineering, or model adaptations. The approach is fast, architecture-agnostic, and compatible with already deployed models for code completion. These findings highlight a practical and effective pathway for integrating language models into already existing tools within IDEs, and ultimately providing smarter and more responsive developer assistance.