# Structure-Aware Corpus Construction and User-Perception-Aligned Metrics for Large-Language-Model Code Completion

arXiv: https://arxiv.org/abs/2505.13073 · HTML: https://mlsanity.com/paper/2505.13073/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 27 references that are in the map.
- First posted on arXiv 19 May 2025.

## Builds on (most cited first, 20 of 20)

- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://mlsanity.com/paper/2109.00859/) (2021)
- [Large Language Models Can Be Easily Distracted by Irrelevant Context](https://mlsanity.com/paper/2302.00093/) (2023)
- [BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions](https://mlsanity.com/paper/2406.15877/) (2024)
- [Unified Pre-training for Program Understanding and Generation](https://mlsanity.com/paper/2103.06333/) (2021)
- [CrossCodeEval: A Diverse and Multilingual Benchmark for Cross-File Code Completion](https://mlsanity.com/paper/2310.11248/) (2023)
- [CoCoMIC: Code Completion By Jointly Modeling In-file and Cross-file Context](https://mlsanity.com/paper/2212.10007/) (2022)
- [RLCoder: Reinforcement Learning for Repository-Level Code Completion](https://mlsanity.com/paper/2407.19487/) (2024)
- [GraphCoder: Enhancing Repository-Level Code Completion via Code Context Graph-based Retrieval and Language Model](https://mlsanity.com/paper/2406.07003/) (2024)
- [Language Models for Code Completion: A Practical Evaluation](https://mlsanity.com/paper/2402.16197/) (2024)
- [AST-T5: Structure-Aware Pretraining for Code Generation and Understanding](https://mlsanity.com/paper/2401.03003/) (2024)
- [Evaluating and Aligning CodeLLMs on Human Preference](https://mlsanity.com/paper/2412.05210/) (2024)
- [STALL+: Boosting LLM-based Repository-level Code Completion with Static Analysis](https://mlsanity.com/paper/2406.10018/) (2024)
- [Learning Autocompletion from Real-World Datasets](https://mlsanity.com/paper/2011.04542/) (2020)
- [Syntax-Aware On-the-Fly Code Completion](https://mlsanity.com/paper/2211.04673/) (2022)
- [All You Need Is Logs: Improving Code Completion by Learning from Anonymous IDE Usage Logs](https://mlsanity.com/paper/2205.10692/) (2022)
- [Enriching Source Code with Contextual Data for Code Completion Models: An Empirical Study](https://mlsanity.com/paper/2304.12269/) (2023)
- [aiXcoder-7B-v2: Training LLMs to Fully Utilize the Long Context in Repository-level Code Completion](https://mlsanity.com/paper/2503.15301/) (2025)
- [Investigating the Performance of Language Models for Completing Code in Functional Programming Languages: a Haskell Case Study](https://mlsanity.com/paper/2403.15185/) (2024)
- [Is Next Token Prediction Sufficient for GPT? Exploration on Code Logic Comprehension](https://mlsanity.com/paper/2404.08885/) (2024)

## Abstract (arXiv)

Code completion technology based on large language model has significantly improved the development efficiency of programmers. However, in practical applications, there remains a gap between current commonly used code completion evaluation metrics and users' actual perception. To address this issue, we propose two evaluation metrics for code completion tasks--LCP and ROUGE-LCP, from the perspective of probabilistic modeling. Furthermore, to tackle the lack of effective structural semantic modeling and cross-module dependency information in LLMs for repository-level code completion scenarios, we propose a data processing method based on a Structure-Preserving and Semantically-Reordered Code Graph (SPSR-Graph). Through theoretical analysis and experimental validation, we demonstrate the superiority of the proposed evaluation metrics in terms of user perception consistency, as well as the effectiveness of the data processing method in enhancing model performance.