GraphCoder: Enhancing Repository-Level Code Completion via Code Context Graph-based Retrieval and Language Model
- URL: http://arxiv.org/abs/2406.07003v1
- Date: Tue, 11 Jun 2024 06:55:32 GMT
- Title: GraphCoder: Enhancing Repository-Level Code Completion via Code Context Graph-based Retrieval and Language Model
- Authors: Wei Liu, Ailun Yu, Daoguang Zan, Bo Shen, Wei Zhang, Haiyan Zhao, Zhi Jin, Qianxiang Wang,
- Abstract summary: GraphCoder is a retrieval-augmented code completion framework.
It uses general code knowledge and the repository-specific knowledge via a graph-based retrieval-generation process.
It achieves higher exact match (EM) on average, with increases of +6.06 in code match and +6.23 in identifier match, while using less time and space.
- Score: 30.625128161499195
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: The performance of repository-level code completion depends upon the effective leverage of both general and repository-specific knowledge. Despite the impressive capability of code LLMs in general code completion tasks, they often exhibit less satisfactory performance on repository-level completion due to the lack of repository-specific knowledge in these LLMs. To address this problem, we propose GraphCoder, a retrieval-augmented code completion framework that leverages LLMs' general code knowledge and the repository-specific knowledge via a graph-based retrieval-generation process. In particular, GraphCoder captures the context of completion target more accurately through code context graph (CCG) that consists of control-flow, data- and control-dependence between code statements, a more structured way to capture the completion target context than the sequence-based context used in existing retrieval-augmented approaches; based on CCG, GraphCoder further employs a coarse-to-fine retrieval process to locate context-similar code snippets with the completion target from the current repository. Experimental results demonstrate both the effectiveness and efficiency of GraphCoder: Compared to baseline retrieval-augmented methods, GraphCoder achieves higher exact match (EM) on average, with increases of +6.06 in code match and +6.23 in identifier match, while using less time and space.
Related papers
- Hierarchical Context Pruning: Optimizing Real-World Code Completion with Repository-Level Pretrained Code LLMs [24.00351065427465]
We propose a strategy named Hierarchical Context Pruning (HCP) to construct completion prompts with high informational code content.
The HCP models the code repository at the function level, maintaining the topological dependencies between code files while removing a large amount of irrelevant code content.
arXiv Detail & Related papers (2024-06-26T12:26:16Z) - On The Importance of Reasoning for Context Retrieval in Repository-Level Code Editing [82.96523584351314]
We decouple the task of context retrieval from the other components of the repository-level code editing pipelines.
We conclude that while the reasoning helps to improve the precision of the gathered context, it still lacks the ability to identify its sufficiency.
arXiv Detail & Related papers (2024-06-06T19:44:17Z) - Dataflow-Guided Retrieval Augmentation for Repository-Level Code Completion [17.4397495929138]
We propose a dataflow-guided retrieval augmentation approach, called DraCo, for repository-level code completion.
Our experiments demonstrate the superior accuracy and applicable efficiency of DraCo, improving code exact match by 3.43% and identifier F1-score by 3.27% on average.
arXiv Detail & Related papers (2024-05-30T07:48:00Z) - RepoHyper: Better Context Retrieval Is All You Need for Repository-Level Code Completion [12.173834895070827]
tool is a framework designed to address the complex challenges associated with repository-level code completion.
Central to tool is the em Repo-level Semantic Graph (RSG), a novel semantic graph structure that encapsulates the vast context of code repositories.
Our evaluations show that tool markedly outperforms existing techniques in repository-level code completion.
arXiv Detail & Related papers (2024-03-10T05:10:34Z) - StepCoder: Improve Code Generation with Reinforcement Learning from
Compiler Feedback [58.20547418182074]
We introduce StepCoder, a novel framework for code generation, consisting of two main components.
CCCS addresses the exploration challenge by breaking the long sequences code generation task into a Curriculum of Code Completion Subtasks.
FGO only optimize the model by masking the unexecuted code segments to provide Fine-Grained Optimization.
Our method improves the ability to explore the output space and outperforms state-of-the-art approaches in corresponding benchmarks.
arXiv Detail & Related papers (2024-02-02T13:14:31Z) - SparseCoder: Identifier-Aware Sparse Transformer for File-Level Code
Summarization [51.67317895094664]
This paper studies file-level code summarization, which can assist programmers in understanding and maintaining large source code projects.
We propose SparseCoder, an identifier-aware sparse transformer for effectively handling long code sequences.
arXiv Detail & Related papers (2024-01-26T09:23:27Z) - LongCoder: A Long-Range Pre-trained Language Model for Code Completion [56.813974784131624]
LongCoder employs a sliding window mechanism for self-attention and introduces two types of globally accessible tokens.
Bridge tokens are inserted throughout the input sequence to aggregate local information and facilitate global interaction.
memory tokens are included to highlight important statements that may be invoked later and need to be memorized.
arXiv Detail & Related papers (2023-06-26T17:59:24Z) - RepoCoder: Repository-Level Code Completion Through Iterative Retrieval
and Generation [96.75695811963242]
RepoCoder is a framework to streamline the repository-level code completion process.
It incorporates a similarity-based retriever and a pre-trained code language model.
It consistently outperforms the vanilla retrieval-augmented code completion approach.
arXiv Detail & Related papers (2023-03-22T13:54:46Z) - ReACC: A Retrieval-Augmented Code Completion Framework [53.49707123661763]
We propose a retrieval-augmented code completion framework, leveraging both lexical copying and referring to code with similar semantics by retrieval.
We evaluate our approach in the code completion task in Python and Java programming languages, achieving a state-of-the-art performance on CodeXGLUE benchmark.
arXiv Detail & Related papers (2022-03-15T08:25:08Z) - deGraphCS: Embedding Variable-based Flow Graph for Neural Code Search [15.19181807445119]
We propose a learnable deep Graph for Code Search (called deGraphCS) to transfer source code into variable-based flow graphs.
We collect a large-scale dataset from GitHub containing 41,152 code snippets written in C language.
arXiv Detail & Related papers (2021-03-24T06:57:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.