Comments (4)
First IQ, Median, and 3rd IQ of G-score performance delta of FFT with LR, RF, and SVM:
from defect-prediction.
1/ The scores are ranked based on the delta with Commit.Guru's Logistic Regression.
2/ With the positive median delta, it shows that FFT won most (SVM: 6/9, RF: 6/9, LR: 6/9 cases) up to 18 max absolute difference.
from defect-prediction.
Summary across learners results
1/ different by small effect between fastread + learner versus keyword + learner.
2/ if fastread with the learner > keyword with the learner
Table is sorted based on FFT.
from defect-prediction.
If fastread with the learner (win + tie) in the comparison to keyword with the learner. Sorted for FFT.
from defect-prediction.
Related Issues (20)
- RQ4
- Disagreement Rate Graphs (9 projects)
- Pseudo labeling using 30% of the FR labeled data to predict on the rest + Effect Size Test
- Comparison between different tricks for data collections! HOT 2
- RQ1: is keyword better than fastread for labeling? HOT 1
- RQ1: How close are Fastread and Keyword to actual Human Labeling HOT 1
- RQ2: How are defect prediction on commit level of human lable data based on Fastread labeled data vs Keyword labeled data? HOT 1
- RQ3: How is Fastread+FFT comparing to Commit.Guru (Keyword+LR) for defect prediction on commit level? HOT 1
- RQ1.2: Which labelling method can generate better data miner for defect prediction tasks? (P_OPT20)
- RQ2: Which predicting method is best for buggy commits? (P_OPT20)
- Commits Messages Bugy HOT 4
- RQ4: Which identification and prediction system perform best for buggy commits? (P_OPT20)
- Defect Prediction on Release Level with Keyword Searches HOT 2
- Defect Prediction on Release Level with Keyword Searches
- Keyword Searches + Commit Level Defect Prediction
- FastRead Commit Messages + Defect Prediction
- State-of-the-art Keywords JIT in comparison with Keywords + Release Level HOT 1
- Standardized Results so far HOT 1
- Effect Size of the remaining 5 datasets.
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from defect-prediction.