Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
json
Languages:
English
Size:
< 1K
Tags:
code-review
defect-detection
software-engineering
label-noise
uncertainty-quantification
python
License:
| [project] | |
| name = "CoReDD" | |
| version = "1.0.0" | |
| description = "Benchmark defect detection in code review with audited label uncertainty." | |
| readme = "README.md" | |
| requires-python = ">=3.10" | |
| license = "CC-BY-4.0" | |
| dependencies = [ | |
| "numpy", | |
| "pygit2", | |
| "scikit-learn", | |
| "tree-sitter", | |
| "tree-sitter-language-pack", | |
| ] | |
| [project.optional-dependencies] | |
| test = ["pytest"] | |
| [build-system] | |
| requires = ["setuptools>=68", "wheel"] | |
| build-backend = "setuptools.build_meta" | |
| [tool.setuptools.packages.find] | |
| where = ["src"] | |
| [tool.pytest.ini_options] | |
| testpaths = ["tests"] | |
| pythonpath = ["src"] | |
| # The tiny fixtures degenerate in many draws (no true nor predicted node); sklearn | |
| # then warns and scores 0.0, which is the intended convention. | |
| filterwarnings = ["ignore::sklearn.exceptions.UndefinedMetricWarning"] | |