Data
pc4

pc4

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/pc1.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. PC4 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_COUNTnumeric61 unique values
0 missing
CALL_PAIRSnumeric22 unique values
0 missing
LOC_CODE_AND_COMMENTnumeric36 unique values
0 missing
LOC_COMMENTSnumeric57 unique values
0 missing
CONDITION_COUNTnumeric41 unique values
0 missing
CYCLOMATIC_COMPLEXITYnumeric43 unique values
0 missing
CYCLOMATIC_DENSITYnumeric70 unique values
0 missing
DECISION_COUNTnumeric23 unique values
0 missing
DECISION_DENSITYnumeric5 unique values
0 missing
DESIGN_COMPLEXITYnumeric31 unique values
0 missing
DESIGN_DENSITYnumeric76 unique values
0 missing
EDGE_COUNTnumeric105 unique values
0 missing
ESSENTIAL_COMPLEXITYnumeric25 unique values
0 missing
ESSENTIAL_DENSITYnumeric2 unique values
0 missing
LOC_EXECUTABLEnumeric107 unique values
0 missing
PARAMETER_COUNTnumeric8 unique values
0 missing
HALSTEAD_CONTENTnumeric1021 unique values
0 missing
HALSTEAD_DIFFICULTYnumeric708 unique values
0 missing
HALSTEAD_EFFORTnumeric1165 unique values
0 missing
HALSTEAD_ERROR_ESTnumeric120 unique values
0 missing
HALSTEAD_LENGTHnumeric336 unique values
0 missing
HALSTEAD_LEVELnumeric40 unique values
0 missing
HALSTEAD_PROG_TIMEnumeric1159 unique values
0 missing
HALSTEAD_VOLUMEnumeric941 unique values
0 missing
MAINTENANCE_SEVERITYnumeric74 unique values
0 missing
MODIFIED_CONDITION_COUNTnumeric28 unique values
0 missing
MULTIPLE_CONDITION_COUNTnumeric40 unique values
0 missing
NODE_COUNTnumeric89 unique values
0 missing
NORMALIZED_CYLOMATIC_COMPLEXITYnumeric67 unique values
0 missing
NUM_OPERANDSnumeric184 unique values
0 missing
NUM_OPERATORSnumeric245 unique values
0 missing
NUM_UNIQUE_OPERANDSnumeric71 unique values
0 missing
NUM_UNIQUE_OPERATORSnumeric38 unique values
0 missing
NUMBER_OF_LINESnumeric171 unique values
0 missing
PERCENT_COMMENTSnumeric394 unique values
0 missing
LOC_TOTALnumeric116 unique values
0 missing

107 properties

1458
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
0.3
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
0.33
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.71
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.
2.82
First quartile of skewness among attributes of the numeric type.
0.35
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.83
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.
0.11
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
6.45
Mean skewness among attributes of the numeric type.
2.02
First quartile of standard deviation of attributes of the numeric type.
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.11
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
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.43
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
1837.83
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
0.3
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
0.15
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
87.79
Percentage of instances belonging to the most frequent class.
Minimal entropy among attributes.
48.05
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.35
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.54
Entropy of the target attribute values.
0.32
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
1280
Number of instances belonging to the most frequent class.
-1.51
Minimum kurtosis among attributes of the numeric type.
7
Second quartile (Median) of means among attributes of the numeric type.
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
0.07
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
0.12
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
771.63
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
5.71
Second quartile (Median) of skewness among attributes of the numeric type.
0.35
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
19505.52
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.
9.49
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.03
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
-0.46
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.14
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.
0.12
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
173.88
Third quartile of kurtosis among attributes of the numeric type.
0.78
Average class difference between consecutive instances.
0.33
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
25.48
Maximum skewness among attributes of the numeric type.
12.21
Percentage of instances belonging to the least frequent class.
97.37
Percentage of numeric attributes.
19.71
Third quartile of means among attributes of the numeric type.
0.83
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
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.11
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
62600.26
Maximum standard deviation of attributes of the numeric type.
178
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.
0.11
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
0.14
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.43
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
9.49
Third quartile of skewness among attributes of the numeric type.
0.3
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
0.33
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
105.09
Mean kurtosis among attributes of the numeric type.
0.17
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
9.74
First quartile of kurtosis among attributes of the numeric type.
26.37
Third quartile of standard deviation of attributes of the numeric type.
0.83
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
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.11
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
583.46
Mean of means among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
0.3
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.47
First quartile of means among attributes of the numeric type.
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.11
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
0.14
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.43
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

17 tasks

102684 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
87 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
1307 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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