TreeRanker: Fast and Model-agnostic Ranking System for Code Suggestions
  in IDEs
Authors: name: Daniele Cipollone, name: Egor Bogomolov, name: Arie van Deursen, name: Maliheh Izadi

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.