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[ICLR 2024] SWE-bench: Can Language Models Resolve Real-world Github Issues?

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LISTING INFORMATION

SWE-bench: An Open Source AI Tool for Software Engineering

Overview

SWE-bench is a cutting-edge open-source AI benchmarking tool designed for evaluating the performance of AI systems in software engineering tasks. Developed by a team of experts including Carlos E. Jimenez and John Yang, SWE-bench provides a comprehensive platform to test how AI can assist in software development, debugging, and code generation.

Recent Updates

  • Multimodal Capabilities (10/2024): SWE-bench now features multimodal functionalities, enabling AI systems to identify and fix bugs visually.
  • SWE-bench Verified (08/2024): A new subset of 500 human-reviewed problems ensures quality and reliability in AI assessments.
  • Docker Support (06/2024): The tool is now containerized for easier and reproducible evaluations across different environments.

How to Use

To get started, users can download SWE-bench from its GitHub repository. Follow the provided documentation for setup instructions and examples of benchmarking AI models.

Purposes

SWE-bench aims to:

  • Assess AI models' efficacy in solving software-related problems.
  • Provide a standardized dataset for researchers and developers.
  • Drive innovation in automated software engineering.

Benefits for Users

  • Enhanced Evaluation: Benchmark your AI models against a diverse set of software challenges.
  • Community Support: Engage with a growing community of developers and researchers.
  • Regular Updates: Stay informed with the latest features and improvements.

Alternatives

While SWE-bench is a robust tool, alternatives like CodeXGLUE and CodeBERT can also be considered for specific AI-driven coding tasks.

Reviews

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