├── 2020213990_周泽龙_2020214022_蔡挺_数据驱动测试.pptx ├── ISDA ├── .gitignore ├── DataProcess.py ├── ImprovedSDA.py ├── README.md ├── data │ ├── CK │ │ ├── CKTest │ │ │ ├── ant1test.mat │ │ │ ├── ivy2test.mat │ │ │ ├── jedit4test.mat │ │ │ ├── lucene2test.mat │ │ │ ├── synapse1test.mat │ │ │ ├── velocity1test.mat │ │ │ └── xalan2test.mat │ │ └── CKTrain │ │ │ ├── ant1train.mat │ │ │ ├── ivy2train.mat │ │ │ ├── jedit4train.mat │ │ │ ├── lucene2train.mat │ │ │ ├── synapse1train.mat │ │ │ ├── velocity1train.mat │ │ │ └── xalan2train.mat │ ├── NASA │ │ ├── NASATest │ │ │ ├── cm1test.mat │ │ │ ├── kc3test.mat │ │ │ ├── mc2test.mat │ │ │ ├── mw1test.mat │ │ │ ├── pc1test.mat │ │ │ ├── pc3test.mat │ │ │ ├── pc4test.mat │ │ │ └── pc5test.mat │ │ └── NASATrain │ │ │ ├── cm1train.mat │ │ │ ├── kc3train.mat │ │ │ ├── mc2train.mat │ │ │ ├── mw1train.mat │ │ │ ├── pc1train.mat │ │ │ ├── pc3train.mat │ │ │ ├── pc4train.mat │ │ │ └── pc5train.mat │ └── 数据格式说明.txt ├── result │ ├── CK │ │ └── ISDA │ │ │ ├── ant1.json │ │ │ ├── ivy2.json │ │ │ ├── jedit4.json │ │ │ ├── lucene2.json │ │ │ ├── synapse1.json │ │ │ ├── velocity1.json │ │ │ └── xalan2.json │ └── NASA │ │ └── ISDA │ │ ├── cm1.json │ │ ├── kc3.json │ │ ├── mc2.json │ │ ├── mw1.json │ │ ├── pc1.json │ │ ├── pc3.json │ │ ├── pc4.json │ │ └── pc5.json └── withinProjectTest.py ├── JSFS ├── 4290-Article Text-7344-1-10-20190706.pdf ├── GBcls.py ├── ck.py ├── config.json ├── data │ ├── CK │ │ ├── CKTest │ │ │ ├── ant1test.mat │ │ │ ├── ivy2test.mat │ │ │ ├── jedit4test.mat │ │ │ ├── lucene2test.mat │ │ │ ├── synapse1test.mat │ │ │ ├── velocity1test.mat │ │ │ └── xalan2test.mat │ │ └── CKTrain │ │ │ ├── ant1train.mat │ │ │ ├── ivy2train.mat │ │ │ ├── jedit4train.mat │ │ │ ├── lucene2train.mat │ │ │ ├── synapse1train.mat │ │ │ ├── velocity1train.mat │ │ │ └── xalan2train.mat │ └── NASA │ │ ├── NASATest │ │ ├── cm1test.mat │ │ ├── kc3test.mat │ │ ├── mc2test.mat │ │ ├── mw1test.mat │ │ ├── pc1test.mat │ │ ├── pc3test.mat │ │ ├── pc4test.mat │ │ └── pc5test.mat │ │ └── NASATrain │ │ ├── cm1train.mat │ │ ├── kc3train.mat │ │ ├── mc2train.mat │ │ ├── mw1train.mat │ │ ├── pc1train.mat │ │ ├── pc3train.mat │ │ ├── pc4train.mat │ │ └── pc5train.mat ├── main.py └── nasa.py ├── MultipleMethods ├── .gitignore ├── DataProcess.py ├── SupervisedMethods.py ├── UnsupervisedMethods.py ├── baggingPU.py ├── data │ ├── CK │ │ ├── CKTest │ │ │ ├── ant1test.mat │ │ │ ├── ivy2test.mat │ │ │ ├── jedit4test.mat │ │ │ ├── lucene2test.mat │ │ │ ├── synapse1test.mat │ │ │ ├── velocity1test.mat │ │ │ └── xalan2test.mat │ │ └── CKTrain │ │ │ ├── ant1train.mat │ │ │ ├── ivy2train.mat │ │ │ ├── jedit4train.mat │ │ │ ├── lucene2train.mat │ │ │ ├── synapse1train.mat │ │ │ ├── velocity1train.mat │ │ │ └── xalan2train.mat │ ├── NASA │ │ ├── NASATest │ │ │ ├── cm1test.mat │ │ │ ├── kc3test.mat │ │ │ ├── mc2test.mat │ │ │ ├── mw1test.mat │ │ │ ├── pc1test.mat │ │ │ ├── pc3test.mat │ │ │ ├── pc4test.mat │ │ │ └── pc5test.mat │ │ └── NASATrain │ │ │ ├── cm1train.mat │ │ │ ├── kc3train.mat │ │ │ ├── mc2train.mat │ │ │ ├── mw1train.mat │ │ │ ├── pc1train.mat │ │ │ ├── pc3train.mat │ │ │ ├── pc4train.mat │ │ │ └── pc5train.mat │ └── 数据格式说明.txt └── result │ ├── CK │ ├── AdaBoostClassifier │ │ ├── ant1.json │ │ ├── ivy2.json │ │ ├── jedit4.json │ │ ├── lucene2.json │ │ ├── synapse1.json │ │ ├── velocity1.json │ │ └── xalan2.json │ ├── BaggingClassifierPU │ │ ├── ant1.json │ │ ├── ivy2.json │ │ ├── jedit4.json │ │ ├── lucene2.json │ │ ├── synapse1.json │ │ ├── velocity1.json │ │ └── xalan2.json │ ├── CBLOF │ │ ├── ant1.json │ │ ├── ivy2.json │ │ ├── jedit4.json │ │ ├── lucene2.json │ │ ├── synapse1.json │ │ ├── velocity1.json │ │ └── xalan2.json │ ├── DecisionTreeClassifier │ │ ├── ant1.json │ │ ├── ivy2.json │ │ ├── jedit4.json │ │ ├── lucene2.json │ │ ├── synapse1.json │ │ ├── velocity1.json │ │ └── xalan2.json │ ├── GaussianNB │ │ ├── ant1.json │ │ ├── ivy2.json │ │ ├── jedit4.json │ │ ├── lucene2.json │ │ ├── synapse1.json │ │ ├── velocity1.json │ │ └── xalan2.json │ ├── GaussianProcessClassifier │ │ ├── ant1.json │ │ ├── ivy2.json │ │ ├── jedit4.json │ │ ├── lucene2.json │ │ ├── synapse1.json │ │ ├── velocity1.json │ │ └── xalan2.json │ ├── KNeighborsClassifier │ │ ├── ant1.json │ │ ├── ivy2.json │ │ ├── jedit4.json │ │ ├── lucene2.json │ │ ├── synapse1.json │ │ ├── velocity1.json │ │ └── xalan2.json │ ├── MLPClassifier │ │ ├── ant1.json │ │ ├── ivy2.json │ │ ├── jedit4.json │ │ ├── lucene2.json │ │ ├── synapse1.json │ │ ├── velocity1.json │ │ └── xalan2.json │ ├── QuadraticDiscriminantAnalysis │ │ ├── ant1.json │ │ ├── ivy2.json │ │ ├── jedit4.json │ │ ├── lucene2.json │ │ ├── synapse1.json │ │ ├── velocity1.json │ │ └── xalan2.json │ ├── RandomForest │ │ ├── ant1.json │ │ ├── ivy2.json │ │ ├── jedit4.json │ │ ├── lucene2.json │ │ ├── synapse1.json │ │ ├── velocity1.json │ │ └── xalan2.json │ ├── RandomForestClassifier │ │ ├── ant1.json │ │ ├── ivy2.json │ │ ├── jedit4.json │ │ ├── lucene2.json │ │ ├── synapse1.json │ │ ├── velocity1.json │ │ └── xalan2.json │ ├── SVC │ │ ├── ant1.json │ │ ├── ivy2.json │ │ ├── jedit4.json │ │ ├── lucene2.json │ │ ├── synapse1.json │ │ ├── velocity1.json │ │ └── xalan2.json │ └── XGBOD │ │ ├── ant1.json │ │ ├── ivy2.json │ │ ├── jedit4.json │ │ ├── lucene2.json │ │ ├── synapse1.json │ │ ├── velocity1.json │ │ └── xalan2.json │ └── NASA │ ├── AdaBoostClassifier │ ├── cm1.json │ ├── kc3.json │ ├── mc2.json │ ├── mw1.json │ ├── pc1.json │ ├── pc3.json │ ├── pc4.json │ └── pc5.json │ ├── BaggingClassifierPU │ ├── cm1.json │ ├── kc3.json │ ├── mc2.json │ ├── mw1.json │ ├── pc1.json │ ├── pc3.json │ ├── pc4.json │ └── pc5.json │ ├── CBLOF │ ├── cm1.json │ ├── kc3.json │ ├── mc2.json │ ├── mw1.json │ ├── pc1.json │ ├── pc3.json │ ├── pc4.json │ └── pc5.json │ ├── DecisionTreeClassifier │ ├── cm1.json │ ├── kc3.json │ ├── mc2.json │ ├── mw1.json │ ├── pc1.json │ ├── pc3.json │ ├── pc4.json │ └── pc5.json │ ├── GaussianNB │ ├── cm1.json │ ├── kc3.json │ ├── mc2.json │ ├── mw1.json │ ├── pc1.json │ ├── pc3.json │ ├── pc4.json │ └── pc5.json │ ├── GaussianProcessClassifier │ ├── cm1.json │ ├── kc3.json │ ├── mc2.json │ ├── mw1.json │ ├── pc1.json │ ├── pc3.json │ ├── pc4.json │ └── pc5.json │ ├── KNeighborsClassifier │ ├── cm1.json │ ├── kc3.json │ ├── mc2.json │ ├── mw1.json │ ├── pc1.json │ ├── pc3.json │ ├── pc4.json │ └── pc5.json │ ├── MLPClassifier │ ├── cm1.json │ ├── kc3.json │ ├── mc2.json │ ├── mw1.json │ ├── pc1.json │ ├── pc3.json │ ├── pc4.json │ └── pc5.json │ ├── QuadraticDiscriminantAnalysis │ ├── cm1.json │ ├── kc3.json │ ├── mc2.json │ ├── mw1.json │ ├── pc1.json │ ├── pc3.json │ ├── pc4.json │ └── pc5.json │ ├── RandomForest │ ├── cm1.json │ ├── kc3.json │ ├── mc2.json │ ├── mw1.json │ ├── pc1.json │ ├── pc3.json │ ├── pc4.json │ └── pc5.json │ ├── RandomForestClassifier │ ├── cm1.json │ ├── kc3.json │ ├── mc2.json │ ├── mw1.json │ ├── pc1.json │ ├── pc3.json │ ├── pc4.json │ └── pc5.json │ ├── SVC │ ├── cm1.json │ ├── kc3.json │ ├── mc2.json │ ├── mw1.json │ ├── pc1.json │ ├── pc3.json │ ├── pc4.json │ └── pc5.json │ └── XGBOD │ ├── cm1.json │ ├── kc3.json │ ├── mc2.json │ ├── mw1.json │ ├── pc1.json │ ├── pc3.json │ ├── pc4.json │ └── pc5.json ├── README.md ├── Reference Method ├── CDDL.rar ├── MKEL.rar └── NGSLP.rar ├── templete.md ├── 对比.xlsx ├── 数据驱动测试报告.assets ├── 1.PNG ├── 2.PNG ├── 3.PNG ├── 4.PNG ├── JSFS1.png ├── JSFS2.png └── JSFS3.png └── 数据驱动测试报告.pdf /2020213990_周泽龙_2020214022_蔡挺_数据驱动测试.pptx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhouzl7/SoftwareDefectPrediction/109885854b8cc8c1d592b22266498ac3f33d34a4/2020213990_周泽龙_2020214022_蔡挺_数据驱动测试.pptx -------------------------------------------------------------------------------- /ISDA/DataProcess.py: -------------------------------------------------------------------------------- 1 | # encoding: utf-8 2 | """ 3 | @author: zhou zelong 4 | @contact: zzl850783164@163.com 5 | @time: 2020/12/3 15:16 6 | @file: DataProcess.py 7 | @desc: 8 | """ 9 | 10 | from scipy.io import loadmat 11 | import numpy as np 12 | 13 | 14 | def load_data(filepath, data_name): 15 | data = loadmat(filepath)[data_name] 16 | return data[:, 0][0], data[:, 1][0], data[:, 2][0] 17 | 18 | 19 | def train_data_process(data): 20 | n, m = data.shape 21 | X1 = np.empty(shape=(0, m - 1)) 22 | X2 = np.empty(shape=(0, m - 1)) 23 | for item in data: 24 | if item[m-1] > 0: 25 | X1 = np.row_stack((X1, item[0:m - 1])) 26 | else: 27 | X2 = np.row_stack((X2, item[0:m - 1])) 28 | return X1, X2 29 | 30 | 31 | def test_data_process(data): 32 | n, m = data.shape 33 | return data[:, 0:m-1], (data[:, m-1] > 0).astype(int) 34 | -------------------------------------------------------------------------------- /ISDA/README.md: -------------------------------------------------------------------------------- 1 | **An Implement of paper "An Improved SDA Based Defect Prediction Framework for Both Within-Project and Cross-Project Class-Imbalance Problems” (IEEE 2017)** 2 | 3 | - [x] `ISDA`-Based Within-Project Prediction 4 | * according the paper, I get the projective transformation matrix V with complex numbers. However, the random forest classifier cannot support complex type. So, I choose the real part of the matrix V. I think something is wrong, but I don’t know how to tackle it. 5 | * refer to paper "Subclass Discriminant Analysis” (IEEE 2006) 6 | - [x] `SSTCA`+`ISDA` for Cross-Project Prediction 7 | - [ ] Use `TCA`+`ISDA` now, need to change `TCA` to `SSTCA` 8 | - refer to paper "Domain Adaptation via Transfer Component Analysis” (IEEE 2011) 9 | 10 | -------------------------------------------------------------------------------- /ISDA/data/CK/CKTest/ant1test.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhouzl7/SoftwareDefectPrediction/109885854b8cc8c1d592b22266498ac3f33d34a4/ISDA/data/CK/CKTest/ant1test.mat -------------------------------------------------------------------------------- /ISDA/data/CK/CKTest/ivy2test.mat: -------------------------------------------------------------------------------- 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13 | 14 | def load_data(filepath, data_name): 15 | data = loadmat(filepath)[data_name] 16 | return data[:, 0][0], data[:, 1][0], data[:, 2][0] 17 | 18 | 19 | def train_data_process(data): 20 | n, m = data.shape 21 | return data[:, 0:m - 1], (data[:, m - 1] > 0).astype(int) 22 | 23 | 24 | def test_data_process(data): 25 | n, m = data.shape 26 | return data[:, 0:m-1], (data[:, m-1] > 0).astype(int) 27 | -------------------------------------------------------------------------------- /MultipleMethods/data/CK/CKTest/ant1test.mat: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/zhouzl7/SoftwareDefectPrediction/109885854b8cc8c1d592b22266498ac3f33d34a4/MultipleMethods/data/CK/CKTest/ant1test.mat -------------------------------------------------------------------------------- /MultipleMethods/data/CK/CKTest/ivy2test.mat: -------------------------------------------------------------------------------- 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/MultipleMethods/result/CK/GaussianNB/lucene2.json: -------------------------------------------------------------------------------- 1 | { 2 | "method": "GaussianNB", 3 | "dataset": "CK", 4 | "subDataset": "lucene2", 5 | "result": [ 6 | { 7 | "train samples": "33", 8 | "defective train samples": "20", 9 | "precision": 0.6632124352331606, 10 | "recall": 0.6994535519125683, 11 | "pf": 0.5241935483870968, 12 | "F-measure": 0.6808510638297872, 13 | "accuracy": 0.6091205211726385, 14 | "AUC": 0.5876300017627357 15 | }, 16 | { 17 | "train samples": "67", 18 | "defective train samples": "40", 19 | "precision": 0.8, 20 | "recall": 0.4171779141104294, 21 | "pf": 0.15454545454545454, 22 | "F-measure": 0.5483870967741935, 23 | "accuracy": 0.5897435897435898, 24 | "AUC": 0.6313162297824875 25 | }, 26 | { 27 | "train samples": "101", 28 | "defective train samples": "60", 29 | "precision": 0.7674418604651163, 30 | "recall": 0.46153846153846156, 31 | "pf": 0.20833333333333334, 32 | "F-measure": 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21 | "pf": 0.23123123123123124, 22 | "F-measure": 0.42362525458248473, 23 | "accuracy": 0.5598755832037325, 24 | "AUC": 0.5521263198682553 25 | }, 26 | { 27 | "train samples": "240", 28 | "defective train samples": "116", 29 | "precision": 0.6451612903225806, 30 | "recall": 0.2952029520295203, 31 | "pf": 0.1506849315068493, 32 | "F-measure": 0.4050632911392405, 33 | "accuracy": 0.5825932504440497, 34 | "AUC": 0.5722590102613355 35 | } 36 | ] 37 | } -------------------------------------------------------------------------------- /MultipleMethods/result/CK/GaussianProcessClassifier/ivy2.json: -------------------------------------------------------------------------------- 1 | { 2 | "method": "GaussianProcessClassifier", 3 | "dataset": "CK", 4 | "subDataset": "ivy2", 5 | "result": [ 6 | { 7 | "train samples": "35", 8 | "defective train samples": "4", 9 | "precision": 0.5, 10 | "recall": 0.19444444444444445, 11 | "pf": 0.02491103202846975, 12 | "F-measure": 0.28, 13 | "accuracy": 0.886435331230284, 14 | "AUC": 0.5847667062079873 15 | }, 16 | { 17 | "train samples": "70", 18 | "defective train samples": "8", 19 | "precision": 0.5, 20 | "recall": 0.1875, 21 | "pf": 0.024, 22 | "F-measure": 0.2727272727272727, 23 | "accuracy": 0.8865248226950354, 24 | "AUC": 0.58175 25 | }, 26 | { 27 | "train samples": "105", 28 | "defective train samples": "12", 29 | "precision": 0.35714285714285715, 30 | "recall": 0.35714285714285715, 31 | "pf": 0.0821917808219178, 32 | "F-measure": 0.35714285714285715, 33 | "accuracy": 0.854251012145749, 34 | "AUC": 0.6374755381604698 35 | } 36 | ] 37 | } -------------------------------------------------------------------------------- /MultipleMethods/result/CK/GaussianProcessClassifier/velocity1.json: -------------------------------------------------------------------------------- 1 | { 2 | "method": "GaussianProcessClassifier", 3 | "dataset": "CK", 4 | "subDataset": "velocity1", 5 | "result": [ 6 | "GaussianProcessClassifier requires 2 or more distinct classes; got 1 class (only class 1 is present)", 7 | "GaussianProcessClassifier requires 2 or more distinct classes; got 1 class (only class 1 is present)", 8 | "GaussianProcessClassifier requires 2 or more distinct classes; got 1 class (only class 1 is present)" 9 | ] 10 | } -------------------------------------------------------------------------------- /MultipleMethods/result/CK/KNeighborsClassifier/ivy2.json: -------------------------------------------------------------------------------- 1 | { 2 | "method": "KNeighborsClassifier", 3 | "dataset": "CK", 4 | "subDataset": "ivy2", 5 | "result": [ 6 | { 7 | "train samples": "35", 8 | "defective train samples": "4", 9 | "precision": 0.2916666666666667, 10 | "recall": 0.19444444444444445, 11 | "pf": 0.060498220640569395, 12 | "F-measure": 0.2333333333333333, 13 | "accuracy": 0.8548895899053628, 14 | "AUC": 0.5669731119019374 15 | }, 16 | { 17 | "train samples": "70", 18 | "defective train samples": "8", 19 | "precision": 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samples": "35", 8 | "defective train samples": "4", 9 | "precision": 0.2608695652173913, 10 | "recall": 0.16666666666666666, 11 | "pf": 0.060498220640569395, 12 | "F-measure": 0.20338983050847456, 13 | "accuracy": 0.8517350157728707, 14 | "AUC": 0.5530842230130486 15 | }, 16 | { 17 | "train samples": "70", 18 | "defective train samples": "8", 19 | "precision": 0.45454545454545453, 20 | "recall": 0.15625, 21 | "pf": 0.024, 22 | "F-measure": 0.2325581395348837, 23 | "accuracy": 0.8829787234042553, 24 | "AUC": 0.566125 25 | }, 26 | { 27 | "train samples": "105", 28 | "defective train samples": "12", 29 | "precision": 0.30303030303030304, 30 | "recall": 0.35714285714285715, 31 | "pf": 0.1050228310502283, 32 | "F-measure": 0.32786885245901637, 33 | "accuracy": 0.8340080971659919, 34 | "AUC": 0.6260600130463144 35 | } 36 | ] 37 | } -------------------------------------------------------------------------------- /MultipleMethods/result/CK/MLPClassifier/lucene2.json: -------------------------------------------------------------------------------- 1 | { 2 | "method": "MLPClassifier", 3 | "dataset": "CK", 4 | "subDataset": "lucene2", 5 | "result": [ 6 | { 7 | "train samples": "33", 8 | "defective train samples": "20", 9 | "precision": 0.6048780487804878, 10 | "recall": 0.6775956284153005, 11 | "pf": 0.6532258064516129, 12 | "F-measure": 0.6391752577319587, 13 | "accuracy": 0.5439739413680782, 14 | "AUC": 0.5121849109818438 15 | }, 16 | { 17 | "train samples": "67", 18 | "defective train samples": "40", 19 | "precision": 0.6848484848484848, 20 | "recall": 0.6932515337423313, 21 | "pf": 0.4727272727272727, 22 | "F-measure": 0.6890243902439025, 23 | "accuracy": 0.6263736263736264, 24 | "AUC": 0.6102621305075293 25 | }, 26 | { 27 | "train samples": "101", 28 | "defective train samples": "60", 29 | "precision": 0.673469387755102, 30 | "recall": 0.6923076923076923, 31 | "pf": 0.5, 32 | "F-measure": 0.6827586206896552, 33 | "accuracy": 0.6150627615062761, 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| "The number of classes has to be greater than one; got 1 class" 9 | ] 10 | } -------------------------------------------------------------------------------- /MultipleMethods/result/CK/SVC/xalan2.json: -------------------------------------------------------------------------------- 1 | { 2 | "method": "SVC", 3 | "dataset": "CK", 4 | "subDataset": "xalan2", 5 | "result": [ 6 | { 7 | "train samples": "79", 8 | "defective train samples": "38", 9 | "precision": 0.6703910614525139, 10 | "recall": 0.3438395415472779, 11 | "pf": 0.15733333333333333, 12 | "F-measure": 0.4545454545454545, 13 | "accuracy": 0.6022099447513812, 14 | "AUC": 0.5932531041069723 15 | }, 16 | { 17 | "train samples": "160", 18 | "defective train samples": "77", 19 | "precision": 0.6282722513089005, 20 | "recall": 0.3870967741935484, 21 | "pf": 0.2132132132132132, 22 | "F-measure": 0.4790419161676647, 23 | "accuracy": 0.5940902021772939, 24 | "AUC": 0.5869417804901677 25 | }, 26 | { 27 | "train samples": "240", 28 | 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