RepoGraph: Enhancing AI Software Engineering with Repository-level Code Graph

• URL: http://arxiv.org/abs/2410.14684v1
• Date: Thu, 03 Oct 2024 05:45:26 GMT
• Title: RepoGraph: Enhancing AI Software Engineering with Repository-level Code Graph
• Authors: Siru Ouyang, Wenhao Yu, Kaixin Ma, Zilin Xiao, Zhihan Zhang, Mengzhao Jia, Jiawei Han, Hongming Zhang, Dong Yu,
• Abstract summary: We present RepoGraph, a plug-in module that manages a repository-level structure for modern AI software engineering solutions. RepoGraph substantially boosts the performance of all systems, leading to a new state-of-the-art among open-source frameworks.
• Score: 63.87660059104077
• License:
• Abstract: Large Language Models (LLMs) excel in code generation yet struggle with modern AI software engineering tasks. Unlike traditional function-level or file-level coding tasks, AI software engineering requires not only basic coding proficiency but also advanced skills in managing and interacting with code repositories. However, existing methods often overlook the need for repository-level code understanding, which is crucial for accurately grasping the broader context and developing effective solutions. On this basis, we present RepoGraph, a plug-in module that manages a repository-level structure for modern AI software engineering solutions. RepoGraph offers the desired guidance and serves as a repository-wide navigation for AI software engineers. We evaluate RepoGraph on the SWE-bench by plugging it into four different methods of two lines of approaches, where RepoGraph substantially boosts the performance of all systems, leading to a new state-of-the-art among open-source frameworks. Our analyses also demonstrate the extensibility and flexibility of RepoGraph by testing on another repo-level coding benchmark, CrossCodeEval. Our code is available at https://github.com/ozyyshr/RepoGraph.

Related papers

• CodexGraph: Bridging Large Language Models and Code Repositories via Code Graph Databases [13.733229886643041]
  Large Language Models (LLMs) excel in stand-alone code tasks like HumanEval and MBPP, but struggle with handling entire code repositories. Similarity-based retrieval often has low recall in complex tasks, while manual tools and APIs are typically task-specific and require expert knowledge. We introduce CodexGraph, a system that integrates LLM agents with graph database interfaces extracted from code repositories.
  arXiv  Detail & Related papers  (2024-08-07T17:13:59Z)
• CodeRAG-Bench: Can Retrieval Augment Code Generation? [78.37076502395699]
  We conduct a systematic, large-scale analysis of code generation using retrieval-augmented generation. We first curate a comprehensive evaluation benchmark, CodeRAG-Bench, encompassing three categories of code generation tasks. We examine top-performing models on CodeRAG-Bench by providing contexts retrieved from one or multiple sources.
  arXiv  Detail & Related papers  (2024-06-20T16:59:52Z)
• How to Understand Whole Software Repository? [64.19431011897515]
  An excellent understanding of the whole repository will be the critical path to Automatic Software Engineering (ASE) We develop a novel method named RepoUnderstander by guiding agents to comprehensively understand the whole repositories. To better utilize the repository-level knowledge, we guide the agents to summarize, analyze, and plan.
  arXiv  Detail & Related papers  (2024-06-03T15:20:06Z)
• RepoHyper: Search-Expand-Refine on Semantic Graphs 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)
• TransformCode: A Contrastive Learning Framework for Code Embedding via Subtree Transformation [9.477734501499274]
  We present TransformCode, a novel framework that learns code embeddings in a contrastive learning manner. Our framework is encoder-agnostic and language-agnostic, which means that it can leverage any encoder model and handle any programming language.
  arXiv  Detail & Related papers  (2023-11-10T09:05:23Z)
• SimTeG: A Frustratingly Simple Approach Improves Textual Graph Learning [131.04781590452308]
  We present SimTeG, a frustratingly Simple approach for Textual Graph learning. We first perform supervised parameter-efficient fine-tuning (PEFT) on a pre-trained LM on the downstream task. We then generate node embeddings using the last hidden states of finetuned LM.
  arXiv  Detail & Related papers  (2023-08-03T07:00:04Z)
• A Unified Active Learning Framework for Annotating Graph Data with Application to Software Source Code Performance Prediction [4.572330678291241]
  We develop a unified active learning framework specializing in software performance prediction. We investigate the impact of using different levels of information for active and passive learning. Our approach aims to improve the investment in AI models for different software performance predictions.
  arXiv  Detail & Related papers  (2023-04-06T14:00:48Z)
• 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)
• Software Vulnerability Detection via Deep Learning over Disaggregated Code Graph Representation [57.92972327649165]
  This work explores a deep learning approach to automatically learn the insecure patterns from code corpora. Because code naturally admits graph structures with parsing, we develop a novel graph neural network (GNN) to exploit both the semantic context and structural regularity of a program.
  arXiv  Detail & Related papers  (2021-09-07T21:24:36Z)
• Graph Neural Network to Dilute Outliers for Refactoring Monolith Application [12.43382998243009]
  We propose a novel method to adapt the recent advancements in graph networks in the context of code to better understand the software. Our solution is able to improve state-of-the-art performance compared to works from both software engineering and existing graph representation based techniques.
  arXiv  Detail & Related papers  (2021-02-07T16:00:27Z)

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.