REPOEXEC: Evaluate Code Generation with a Repository-Level Executable Benchmark

• URL: http://arxiv.org/abs/2406.11927v2
• Date: Wed, 19 Jun 2024 05:27:32 GMT
• Title: REPOEXEC: Evaluate Code Generation with a Repository-Level Executable Benchmark
• Authors: Nam Le Hai, Dung Manh Nguyen, Nghi D. Q. Bui,
• Abstract summary: We introduce RepoExec, a novel benchmark for evaluating code generation at the repository-level scale. RepoExec focuses on three main aspects: executability, functional correctness through automated test case generation with high coverage rate, and carefully crafted cross-file contexts to accurately generate code.
• Score: 5.641402231731082
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: The ability of CodeLLMs to generate executable and functionally correct code at the repository-level scale remains largely unexplored. We introduce RepoExec, a novel benchmark for evaluating code generation at the repository-level scale. RepoExec focuses on three main aspects: executability, functional correctness through automated test case generation with high coverage rate, and carefully crafted cross-file contexts to accurately generate code. Our work explores a controlled scenario where developers specify necessary code dependencies, challenging the model to integrate these accurately. Experiments show that while pretrained LLMs outperform instruction-tuned models in correctness, the latter excel in utilizing provided dependencies and demonstrating debugging capabilities. We also introduce a new instruction-tuned dataset that focuses on code dependencies and demonstrate that CodeLLMs fine-tuned on our dataset have a better capability to leverage these dependencies effectively. RepoExec aims to provide a comprehensive evaluation of code functionality and alignment with developer intent, paving the way for more reliable and applicable CodeLLMs in real-world scenarios. The dataset and source code can be found at~\url{https://github.com/FSoft-AI4Code/RepoExec}.

Related papers

• Turning the Tide: Repository-based Code Reflection [52.13709676656648]
  We introduce LiveRepoReflection, a benchmark for evaluating code understanding and generation in multi-file repository contexts.<n>1,888 rigorously filtered test cases across $6$ programming languages to ensure diversity, correctness, and high difficulty.<n>We also create RepoReflection-Instruct, a large-scale, quality-filtered instruction-tuning dataset derived from diverse sources.
  arXiv  Detail & Related papers  (2025-07-14T02:36:27Z)
• FEA-Bench: A Benchmark for Evaluating Repository-Level Code Generation for Feature Implementation [26.14778133391999]
  FEA-Bench is a benchmark designed to assess the ability of large language models to perform incremental development within code repositories. We collect pull requests from 83 GitHub repositories and use rule-based and intent-based filtering to construct task instances focused on new feature development.
  arXiv  Detail & Related papers  (2025-03-09T16:11:57Z)
• Code Summarization Beyond Function Level [0.213063058314067]
  This study investigated the effectiveness of code summarization models beyond the function level. The fine-tuned state-of-the-art CodeT5+ base model excelled in code summarization. Repository-level summarization exhibited promising potential but requires significant computational resources.
  arXiv  Detail & Related papers  (2025-02-23T20:31:21Z)
• ExecRepoBench: Multi-level Executable Code Completion Evaluation [45.963424627710765]
  We introduce a novel framework for enhancing code completion in software development through the creation of a repository-level benchmark ExecRepoBench. We present a multi-level grammar-based completion methodology conditioned on the abstract syntax tree to mask code fragments at various logical units. 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.
  arXiv  Detail & Related papers  (2024-12-16T17:14:35Z)
• ContextModule: Improving Code Completion via Repository-level Contextual Information [11.459065573651348]
  ContextModule improves the relevance and precision of generated code. We implement performance optimizations, such as index caching, to ensure the system meets the latency constraints of real-world coding environments.
  arXiv  Detail & Related papers  (2024-12-11T03:15:49Z)
• Codev-Bench: How Do LLMs Understand Developer-Centric Code Completion? [60.84912551069379]
  We present the Code-Development Benchmark (Codev-Bench), a fine-grained, real-world, repository-level, and developer-centric evaluation framework. Codev-Agent is an agent-based system that automates repository crawling, constructs execution environments, extracts dynamic calling chains from existing unit tests, and generates new test samples to avoid data leakage.
  arXiv  Detail & Related papers  (2024-10-02T09:11:10Z)
• 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)
• GraphCoder: Enhancing Repository-Level Code Completion via Code Context Graph-based Retrieval and Language Model [30.625128161499195]
  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.
  arXiv  Detail & Related papers  (2024-06-11T06:55:32Z)
• 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)
• Class-Level Code Generation from Natural Language Using Iterative, Tool-Enhanced Reasoning over Repository [4.767858874370881]
  We introduce RepoClassBench, a benchmark designed to rigorously evaluate LLMs in generating class-level code within real-world repositories. RepoClassBench includes "Natural Language to Class generation" tasks across Java, Python & C# from a selection of repositories. We introduce Retrieve-Repotools-Reflect (RRR), a novel approach that equips LLMs with static analysis tools to iteratively navigate & reason about repository-level context.
  arXiv  Detail & Related papers  (2024-04-22T03:52:54Z)
• Enhancing LLM-Based Coding Tools through Native Integration of IDE-Derived Static Context [41.91246546266515]
  We argue that Integrated Development Environments (IDEs) can provide direct, accurate and real-time cross-file information for repository-level code completion. We propose a framework that leveragesIDE native static contexts for cross-context construction and diagnosis results for self-refinement.
  arXiv  Detail & Related papers  (2024-02-06T01:59:41Z)
• 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)
• 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)

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.