seart-group/DL4SE
Building Training Datasets for Deep Learning Models in Software Engineering and Empirical Software Engineering Research
The SEART Data Hub helps software engineering researchers and practitioners create extensive datasets from GitHub source code. It takes raw code from repositories and processes it to identify specific elements like test code or boilerplate, outputting structured datasets suitable for empirical studies or training deep learning models for software development tasks. This tool is designed for academics and industry researchers focused on improving software engineering through data-driven approaches.
No commits in the last 6 months.
Use this if you need to build large-scale, specialized datasets from public GitHub repositories for software engineering research or to train AI models for coding tasks.
Not ideal if you are looking for a general-purpose code analysis tool or if your data sources are not GitHub repositories.
Stars
26
Forks
4
Language
Java
License
MIT
Category
Last pushed
Jun 26, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/seart-group/DL4SE"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
open-edge-platform/datumaro
Dataset Management Framework, a Python library and a CLI tool to build, analyze and manage...
explosion/ml-datasets
🌊 Machine learning dataset loaders for testing and example scripts
webdataset/webdataset
A high-performance Python-based I/O system for large (and small) deep learning problems, with...
tensorflow/datasets
TFDS is a collection of datasets ready to use with TensorFlow, Jax, ...
mlcommons/croissant
Croissant is a high-level format for machine learning datasets that brings together four rich layers.