Data
pc3

pc3

active ARFF Publicly available Visibility: public Uploaded 06-10-2014 by Joaquin Vanschoren
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  • mythbusting_1 OpenML-CC18 OpenML100 study_1 study_123 study_14 study_15 study_20 study_34 study_41 study_52 study_7 study_98 study_99
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Author: Mike Chapman, NASA Source: [tera-PROMISE](http://openscience.us/repo/defect/mccabehalsted/pc3.html) - 2004 Please cite: Sayyad Shirabad, J. and Menzies, T.J. (2005) The PROMISE Repository of Software Engineering Databases. School of Information Technology and Engineering, University of Ottawa, Canada. PC3 Software defect prediction One of the NASA Metrics Data Program defect data sets. Data from flight software for earth orbiting satellite. Data comes from McCabe and Halstead features extractors of source code. These features were defined in the 70s in an attempt to objectively characterize code features that are associated with software quality. ### Relevant papers - Shepperd, M. and Qinbao Song and Zhongbin Sun and Mair, C. (2013) Data Quality: Some Comments on the NASA Software Defect Datasets, IEEE Transactions on Software Engineering, 39. - Tim Menzies and Justin S. Di Stefano (2004) How Good is Your Blind Spot Sampling Policy? 2004 IEEE Conference on High Assurance Software Engineering. - T. Menzies and J. DiStefano and A. Orrego and R. Chapman (2004) Assessing Predictors of Software Defects", Workshop on Predictive Software Models, Chicago

38 features

c (target)nominal2 unique values
0 missing
LOC_BLANKnumeric54 unique values
0 missing
BRANCH_COUNTnumeric72 unique values
0 missing
CALL_PAIRSnumeric20 unique values
0 missing
LOC_CODE_AND_COMMENTnumeric25 unique values
0 missing
LOC_COMMENTSnumeric58 unique values
0 missing
CONDITION_COUNTnumeric69 unique values
0 missing
CYCLOMATIC_COMPLEXITYnumeric52 unique values
0 missing
CYCLOMATIC_DENSITYnumeric77 unique values
0 missing
DECISION_COUNTnumeric45 unique values
0 missing
DECISION_DENSITYnumeric52 unique values
0 missing
DESIGN_COMPLEXITYnumeric33 unique values
0 missing
DESIGN_DENSITYnumeric78 unique values
0 missing
EDGE_COUNTnumeric126 unique values
0 missing
ESSENTIAL_COMPLEXITYnumeric25 unique values
0 missing
ESSENTIAL_DENSITYnumeric61 unique values
0 missing
LOC_EXECUTABLEnumeric118 unique values
0 missing
PARAMETER_COUNTnumeric8 unique values
0 missing
HALSTEAD_CONTENTnumeric1174 unique values
0 missing
HALSTEAD_DIFFICULTYnumeric822 unique values
0 missing
HALSTEAD_EFFORTnumeric1329 unique values
0 missing
HALSTEAD_ERROR_ESTnumeric139 unique values
0 missing
HALSTEAD_LENGTHnumeric357 unique values
0 missing
HALSTEAD_LEVELnumeric45 unique values
0 missing
HALSTEAD_PROG_TIMEnumeric1318 unique values
0 missing
HALSTEAD_VOLUMEnumeric1055 unique values
0 missing
MAINTENANCE_SEVERITYnumeric81 unique values
0 missing
MODIFIED_CONDITION_COUNTnumeric50 unique values
0 missing
MULTIPLE_CONDITION_COUNTnumeric68 unique values
0 missing
NODE_COUNTnumeric103 unique values
0 missing
NORMALIZED_CYLOMATIC_COMPLEXITYnumeric68 unique values
0 missing
NUM_OPERANDSnumeric227 unique values
0 missing
NUM_OPERATORSnumeric259 unique values
0 missing
NUM_UNIQUE_OPERANDSnumeric117 unique values
0 missing
NUM_UNIQUE_OPERATORSnumeric43 unique values
0 missing
NUMBER_OF_LINESnumeric170 unique values
0 missing
PERCENT_COMMENTSnumeric377 unique values
0 missing
LOC_TOTALnumeric123 unique values
0 missing

107 properties

1563
Number of instances (rows) of the dataset.
38
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
37
Number of numeric attributes.
1
Number of nominal attributes.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.11
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.53
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
2
Average number of distinct values among the attributes of the nominal type.
2.63
First quartile of skewness among attributes of the numeric type.
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0
Standard deviation of the number of distinct values among attributes of the nominal type.
0.12
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
10.32
Mean skewness among attributes of the numeric type.
2.06
First quartile of standard deviation of attributes of the numeric type.
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.1
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.12
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
10334.5
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.11
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.12
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
89.76
Percentage of instances belonging to the most frequent class.
Minimal entropy among attributes.
144.27
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.48
Entropy of the target attribute values.
0.26
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
1403
Number of instances belonging to the most frequent class.
-1.48
Minimum kurtosis among attributes of the numeric type.
7.64
Second quartile (Median) of means among attributes of the numeric type.
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
0.12
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.11
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.1
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
1039.34
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
9.78
Second quartile (Median) of skewness among attributes of the numeric type.
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
34072.82
Maximum of means among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
2.63
Percentage of binary attributes.
15.93
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.59
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.02
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
-0.59
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.16
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
2
The maximum number of distinct values among attributes of the nominal type.
0.13
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
407.27
Third quartile of kurtosis among attributes of the numeric type.
0.81
Average class difference between consecutive instances.
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.53
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
30.49
Maximum skewness among attributes of the numeric type.
10.24
Percentage of instances belonging to the least frequent class.
97.37
Percentage of numeric attributes.
22.68
Third quartile of means among attributes of the numeric type.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.59
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.12
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
358165.94
Maximum standard deviation of attributes of the numeric type.
160
Number of instances belonging to the least frequent class.
2.63
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.1
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.16
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.12
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
16.3
Third quartile of skewness among attributes of the numeric type.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.53
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
238.95
Mean kurtosis among attributes of the numeric type.
0.48
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
9.98
First quartile of kurtosis among attributes of the numeric type.
43.86
Third quartile of standard deviation of attributes of the numeric type.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.59
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.12
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
1008.15
Mean of means among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
0.07
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.44
First quartile of means among attributes of the numeric type.
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.1
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.16
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.12
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

17 tasks

136054 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: c
193 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: c
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: c
0 runs - estimation_procedure: 33% Holdout set - target_feature: c
86 runs - estimation_procedure: 10-fold Learning Curve - target_feature: c
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: c
0 runs - target_feature: c
1310 runs - target_feature: c
1308 runs - target_feature: c
0 runs - target_feature: c
0 runs - target_feature: c
0 runs - target_feature: c
0 runs - target_feature: c
0 runs - target_feature: c
0 runs - target_feature: c
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