- Developed & tested under Python 3.9.1
- To develop this module, set up the required dependencies:
python -m pip install -r requirements.txt
git clone https://github.com/Suresoft-GLaDOS/SBFL
cd SBFL
python -m pip install --upgrade pip
python -m pip install .Or simply do:
pip install git+https://github.com/Suresoft-GLaDOS/SBFL.gitimport numpy as np
from sbfl.base import SBFL
if __name__ == "__main__":
"""
X: coverage data
y: test results
"""
X = np.array([
[1,0,1], # coverage of test t0
[0,0,1], # coverage of test t1
[1,1,0] # coverage of test t2
], dtype=bool)
y = np.array([
1, # t0: PASS
0, # t1: FAIL
1 # t2: PASS
], dtype=bool)
"""
Calculate the suspiciousness scores
"""
sbfl = SBFL(formula='Ochiai')
sbfl.fit(X, y)
print(sbfl.ranks(method='max'))See the full example usage of this engine in ./main.ipynb.
- If you implement new functionality, please add the test cases for it.
- After any code change, make sure that the entire test suite passes.
# without measuring coverage
python -m pytest
# with measuring coverage
python -m pip install coverage
python -m coverage run --source=sbfl -m pytest
python -m coverage reportpip install mutmut
mutmut run
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