ExecRepoBench: Multi-level Executable Code Completion Evaluation

• URL: http://arxiv.org/abs/2412.11990v1
• Date: Mon, 16 Dec 2024 17:14:35 GMT
• Title: ExecRepoBench: Multi-level Executable Code Completion Evaluation
• Authors: Jian Yang, Jiajun Zhang, Jiaxi Yang, Ke Jin, Lei Zhang, Qiyao Peng, Ken Deng, Yibo Miao, Tianyu Liu, Zeyu Cui, Binyuan Hui, Junyang Lin,
• Abstract summary: We introduce a novel framework for enhancing code completion in software development through the creation of a repository-level benchmark ExecRepoBench.<n>We present a multi-level grammar-based completion methodology conditioned on the abstract syntax tree to mask code fragments at various logical units.<n>Then, we fine-tune the open-source LLM with 7B parameters on Repo-Instruct to produce a strong code completion baseline model Qwen2.5-Coder-Instruct-C.
• Score: 45.963424627710765
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Code completion has become an essential tool for daily software development. Existing evaluation benchmarks often employ static methods that do not fully capture the dynamic nature of real-world coding environments and face significant challenges, including limited context length, reliance on superficial evaluation metrics, and potential overfitting to training datasets. In this work, we introduce a novel framework for enhancing code completion in software development through the creation of a repository-level benchmark ExecRepoBench and the instruction corpora Repo-Instruct, aim at improving the functionality of open-source large language models (LLMs) in real-world coding scenarios that involve complex interdependencies across multiple files. ExecRepoBench includes 1.2K samples from active Python repositories. Plus, we present a multi-level grammar-based completion methodology conditioned on the abstract syntax tree to mask code fragments at various logical units (e.g. statements, expressions, and functions). Then, we fine-tune the open-source LLM with 7B parameters on Repo-Instruct to produce a strong code completion baseline model Qwen2.5-Coder-Instruct-C based on the open-source model. Qwen2.5-Coder-Instruct-C is rigorously evaluated against existing benchmarks, including MultiPL-E and ExecRepoBench, which consistently outperforms prior baselines across all programming languages. The deployment of \ourmethod{} can be used as a high-performance, local service for programming development\footnote{\url{https://execrepobench.github.io/}}.

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