Codev-Bench: How Do LLMs Understand Developer-Centric Code Completion?
- URL: http://arxiv.org/abs/2410.01353v3
- Date: Thu, 24 Oct 2024 06:24:56 GMT
- Title: Codev-Bench: How Do LLMs Understand Developer-Centric Code Completion?
- Authors: Zhenyu Pan, Rongyu Cao, Yongchang Cao, Yingwei Ma, Binhua Li, Fei Huang, Han Liu, Yongbin Li,
- Abstract summary: 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.
- Score: 60.84912551069379
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Code completion, a key downstream task in code generation, is one of the most frequent and impactful methods for enhancing developer productivity in software development. As intelligent completion tools evolve, we need a robust evaluation benchmark that enables meaningful comparisons between products and guides future advancements. However, existing benchmarks focus more on coarse-grained tasks without industrial analysis resembling general code generation rather than the real-world scenarios developers encounter. Moreover, these benchmarks often rely on costly and time-consuming human annotation, and the standalone test cases fail to leverage minimal tests for maximum repository-level understanding and code coverage. To address these limitations, we first analyze business data from an industrial code completion tool and redefine the evaluation criteria to better align with the developer's intent and desired completion behavior throughout the coding process. Based on these insights, we introduce Codev-Agent, 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, ensuring fair and effective comparisons. Using Codev-Agent, we present the Code-Development Benchmark (Codev-Bench), a fine-grained, real-world, repository-level, and developer-centric evaluation framework. Codev-Bench assesses whether a code completion tool can capture a developer's immediate intent and suggest appropriate code across diverse contexts, providing a more realistic benchmark for code completion in modern software development.
Related papers
- Bridging LLM-Generated Code and Requirements: Reverse Generation technique and SBC Metric for Developer Insights [0.0]
This paper introduces a novel scoring mechanism called the SBC score.
It is based on a reverse generation technique that leverages the natural language generation capabilities of Large Language Models.
Unlike direct code analysis, our approach reconstructs system requirements from AI-generated code and compares them with the original specifications.
arXiv Detail & Related papers (2025-02-11T01:12:11Z) - Commit0: Library Generation from Scratch [77.38414688148006]
Commit0 is a benchmark that challenges AI agents to write libraries from scratch.
Agents are provided with a specification document outlining the library's API as well as a suite of interactive unit tests.
Commit0 also offers an interactive environment where models receive static analysis and execution feedback on the code they generate.
arXiv Detail & Related papers (2024-12-02T18:11:30Z) - CodeTree: Agent-guided Tree Search for Code Generation with Large Language Models [106.11371409170818]
Large language models (LLMs) can act as agents with capabilities to self-refine and improve generated code autonomously.
We propose CodeTree, a framework for LLM agents to efficiently explore the search space in different stages of the code generation process.
Specifically, we adopted a unified tree structure to explicitly explore different coding strategies, generate corresponding coding solutions, and subsequently refine the solutions.
arXiv Detail & Related papers (2024-11-07T00:09:54Z) - Contextualized Data-Wrangling Code Generation in Computational Notebooks [131.26365849822932]
We propose an automated approach, CoCoMine, to mine data-wrangling code generation examples with clear multi-modal contextual dependency.
We construct CoCoNote, a dataset containing 58,221 examples for Contextualized Data-wrangling Code generation in Notebooks.
Experiment results demonstrate the significance of incorporating data context in data-wrangling code generation.
arXiv Detail & Related papers (2024-09-20T14:49:51Z) - ComplexCodeEval: A Benchmark for Evaluating Large Code Models on More Complex Code [29.178248778212588]
ComplexCodeEval is a benchmark designed to assess large language models (LLMs) in various development tasks.
It includes 3,897 Java samples and 7,184 Python samples from high-star GitHub repositories.
arXiv Detail & Related papers (2024-09-16T13:43:04Z) - RepoMasterEval: Evaluating Code Completion via Real-World Repositories [12.176098357240095]
RepoMasterEval is a novel benchmark for evaluating code completion models constructed from real-world Python and TypeScript repositories.
To improve test accuracy of model generated code, we employ mutation testing to measure the effectiveness of the test cases.
Our empirical evaluation on 6 state-of-the-art models shows that test argumentation is critical in improving the accuracy of the benchmark.
arXiv Detail & Related papers (2024-08-07T03:06:57Z) - On the Impacts of Contexts on Repository-Level Code Generation [5.641402231731082]
We present RepoExec, a novel benchmark designed to evaluate repository-level code generation.
We focus on three key aspects: executability, functional correctness through comprehensive test case generation, and accurate utilization of cross-file contexts.
arXiv Detail & Related papers (2024-06-17T10:45:22Z) - 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.