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:
File size: 804 Bytes
ecaa1ff 7deba50 ecaa1ff 7deba50 ecaa1ff 11d2b0b 7deba50 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | [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"]
|