Beyond More Context: How Granularity and Order Drive Code Completion Quality
Bibliographic record
Abstract
Context plays an important role in the quality of code completion, as Large Language Models (LLMs) require sufficient and relevant information to assist developers in code generation tasks. However, composing a relevant context for code completion poses challenges in large repositories: First, the limited context length of LLMs makes it impractical to include all repository files. Second, the quality of generated code is highly sensitive to noisy or irrelevant context. In this paper, we present our approach for the ASE 2025 Context Collection Challenge. The challenge entails outperforming JetBrains baselines by designing effective retrieval and context collection strategies. We develop and evaluate a series of experiments that involve retrieval strategies at both the file and chunk levels. We focus our initial experiments on examining the impact of context size and file ordering on LLM performance. Our results show that the amount and order of context can significantly influence the performance of the models. We introduce chunkbased retrieval using static analysis, achieving a 6% improvement over our best file-retrieval strategy and 16% over the no-context baseline for Python in the initial phase of the competition. Our results highlight the importance of retrieval granularity, ordering and hybrid strategies in developing effective context collection pipelines for real-world development scenarios.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".