⚙️ SWE-rebench: Nebius AI R&D team presents new dataset for SWE tasks.
Researchers built an automated system to collect and validate thousands of real-world tasks from GitHub, designed for training and evaluation of LLMs in software engineering.
Main features of the system:
1️⃣ Automatic data collection: Continuously extracts issue-PR pairs from Python repositories.
2️⃣ LLM-based environment setup: LLM analyzes repositories, creates install instructions, and updates them if errors happen.
3️⃣ Execution-based validation: Each task is tested by automatic setup, test run, and dependency freezing to make it reproducible.
4️⃣ LLM quality annotation: Tasks are labeled for clarity, difficulty, and test correctness to support filtering.
Result:
SWE-rebench dataset: 21,000+ ready-to-use interactive tasks.
Continuous updates: Fresh data is added regularly.
Transparent evaluation: Tasks are used for public SWE-rebench leaderboard.
🚀 SWE-rebench gives researchers and developers real and validated tasks to work with LLMs in SWE field.
Technical report: arXiv
Dataset: SWE-rebench
Researchers built an automated system to collect and validate thousands of real-world tasks from GitHub, designed for training and evaluation of LLMs in software engineering.
Main features of the system:
1️⃣ Automatic data collection: Continuously extracts issue-PR pairs from Python repositories.
2️⃣ LLM-based environment setup: LLM analyzes repositories, creates install instructions, and updates them if errors happen.
3️⃣ Execution-based validation: Each task is tested by automatic setup, test run, and dependency freezing to make it reproducible.
4️⃣ LLM quality annotation: Tasks are labeled for clarity, difficulty, and test correctness to support filtering.
Result:
SWE-rebench dataset: 21,000+ ready-to-use interactive tasks.
Continuous updates: Fresh data is added regularly.
Transparent evaluation: Tasks are used for public SWE-rebench leaderboard.
🚀 SWE-rebench gives researchers and developers real and validated tasks to work with LLMs in SWE field.
Technical report: arXiv
Dataset: SWE-rebench
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⚙️ SWE-rebench: Nebius AI R&D team presents new dataset for SWE tasks.
Researchers built an automated system to collect and validate thousands of real-world tasks from GitHub, designed for training and evaluation of LLMs in software engineering.
Main features of the system:
1️⃣ Automatic data collection: Continuously extracts issue-PR pairs from Python repositories.
2️⃣ LLM-based environment setup: LLM analyzes repositories, creates install instructions, and updates them if errors happen.
3️⃣ Execution-based validation: Each task is tested by automatic setup, test run, and dependency freezing to make it reproducible.
4️⃣ LLM quality annotation: Tasks are labeled for clarity, difficulty, and test correctness to support filtering.
Result:
SWE-rebench dataset: 21,000+ ready-to-use interactive tasks.
Continuous updates: Fresh data is added regularly.
Transparent evaluation: Tasks are used for public SWE-rebench leaderboard.
🚀 SWE-rebench gives researchers and developers real and validated tasks to work with LLMs in SWE field.
Technical report: arXiv
Dataset: SWE-rebench
Researchers built an automated system to collect and validate thousands of real-world tasks from GitHub, designed for training and evaluation of LLMs in software engineering.
Main features of the system:
1️⃣ Automatic data collection: Continuously extracts issue-PR pairs from Python repositories.
2️⃣ LLM-based environment setup: LLM analyzes repositories, creates install instructions, and updates them if errors happen.
3️⃣ Execution-based validation: Each task is tested by automatic setup, test run, and dependency freezing to make it reproducible.
4️⃣ LLM quality annotation: Tasks are labeled for clarity, difficulty, and test correctness to support filtering.
Result:
SWE-rebench dataset: 21,000+ ready-to-use interactive tasks.
Continuous updates: Fresh data is added regularly.
Transparent evaluation: Tasks are used for public SWE-rebench leaderboard.
🚀 SWE-rebench gives researchers and developers real and validated tasks to work with LLMs in SWE field.
Technical report: arXiv
Dataset: SWE-rebench
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