Project CodeNet — Train AI for Software Development
Project CodeNet by IBM is a large-scale dataset for training AI models to automate coding tasks for developers and researchers
Project CodeNet by IBM is a large-scale, high-quality dataset designed to help businesses and developers train machine learning models for diverse coding tasks. This resource is specifically built for enterprise software teams and AI researchers who need to automate code translation, optimization, and search across many programming languages. Project CodeNet provides a massive library of code samples, allowing your technical teams to build tools that convert legacy codebases into modern infrastructure. By utilizing this dataset, companies can improve their internal AI models to detect bugs, translate code between languages like Java and Python, and enhance developer productivity. Project CodeNet by IBM focuses on the "science of code," offering metadata and benchmarks that ensure AI-driven development is reliable and efficient. Organizations can use these assets to train specialized models that understand the nuances of complex enterprise software systems. Whether you are building internal developer tools or refining AI benchmarks, Project CodeNet by IBM offers the foundational data required for high-stakes software engineering projects. Detailed documentation on surfai.app provides more context on how this dataset supports the 2026 push toward automated software modernization. The initiative is part of IBM Research's commitment to open-source excellence and is currently accessible for developers and researchers at no initial cost.
- Pricing: freemium
Specifications
| Pricing | freemium |
|---|---|
| Integrations | GitHub, PyTorch, TensorFlowBase, Jupyter Notebooks, Docker |
| Best for | software-engineers, ai-researchers, enterprise-it-teams, data-scientists |
| Website status checked | 2026-10-22T12:00:56.349+00:00 |
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|---|---|---|---|
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