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mc2

mc2

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  • mythbusting_1 PROMISE study_1 study_123 study_15 study_20 study_41 study_52 study_7 study_88 study_236
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Author: Mike Chapman, NASA Source: [tera-PROMISE](http://openscience.us/repo/defect/mccabehalsted/mc2.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. MC2 Software defect prediction One of the NASA Metrics Data Program defect data sets. The specific type of software is unknown. 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

40 features

c (target)nominal2 unique values
0 missing
HALSTEAD_EFFORTnumeric157 unique values
0 missing
HALSTEAD_DIFFICULTYnumeric141 unique values
0 missing
HALSTEAD_ERROR_ESTnumeric71 unique values
0 missing
HALSTEAD_LENGTHnumeric122 unique values
0 missing
HALSTEAD_LEVELnumeric33 unique values
0 missing
HALSTEAD_PROG_TIMEnumeric157 unique values
0 missing
HALSTEAD_VOLUMEnumeric151 unique values
0 missing
MAINTENANCE_SEVERITYnumeric37 unique values
0 missing
MODIFIED_CONDITION_COUNTnumeric28 unique values
0 missing
MULTIPLE_CONDITION_COUNTnumeric37 unique values
0 missing
NODE_COUNTnumeric55 unique values
0 missing
NORMALIZED_CYLOMATIC_COMPLEXITYnumeric33 unique values
0 missing
NUM_OPERANDSnumeric95 unique values
0 missing
NUM_OPERATORSnumeric102 unique values
0 missing
NUM_UNIQUE_OPERANDSnumeric55 unique values
0 missing
NUM_UNIQUE_OPERATORSnumeric32 unique values
0 missing
NUMBER_OF_LINESnumeric88 unique values
0 missing
PERCENT_COMMENTSnumeric97 unique values
0 missing
LOC_TOTALnumeric73 unique values
0 missing
DESIGN_COMPLEXITYnumeric15 unique values
0 missing
BRANCH_COUNTnumeric32 unique values
0 missing
CALL_PAIRSnumeric13 unique values
0 missing
LOC_CODE_AND_COMMENTnumeric16 unique values
0 missing
LOC_COMMENTSnumeric41 unique values
0 missing
CONDITION_COUNTnumeric38 unique values
0 missing
CYCLOMATIC_COMPLEXITYnumeric28 unique values
0 missing
CYCLOMATIC_DENSITYnumeric43 unique values
0 missing
DECISION_COUNTnumeric23 unique values
0 missing
DECISION_DENSITYnumeric26 unique values
0 missing
LOC_BLANKnumeric35 unique values
0 missing
DESIGN_DENSITYnumeric38 unique values
0 missing
EDGE_COUNTnumeric62 unique values
0 missing
ESSENTIAL_COMPLEXITYnumeric17 unique values
0 missing
ESSENTIAL_DENSITYnumeric30 unique values
0 missing
LOC_EXECUTABLEnumeric68 unique values
0 missing
PARAMETER_COUNTnumeric6 unique values
0 missing
GLOBAL_DATA_COMPLEXITYnumeric26 unique values
0 missing
GLOBAL_DATA_DENSITYnumeric31 unique values
0 missing
HALSTEAD_CONTENTnumeric155 unique values
0 missing
Show all 40 features

107 properties

NumberOfInstances
161
Number of instances (rows) of the dataset.
NumberOfFeatures
40
Number of attributes (columns) of the dataset.
NumberOfClasses
2
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
39
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
AutoCorrelation
0.56
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
0.52
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
CfsSubsetEval_DecisionStumpErrRate
0.33
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
CfsSubsetEval_DecisionStumpKappa
0.04
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
CfsSubsetEval_NaiveBayesAUC
0.52
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
CfsSubsetEval_NaiveBayesErrRate
0.33
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
CfsSubsetEval_NaiveBayesKappa
0.04
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
CfsSubsetEval_kNN1NAUC
0.52
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
CfsSubsetEval_kNN1NErrRate
0.33
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
CfsSubsetEval_kNN1NKappa
0.04
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
ClassEntropy
0.91
Entropy of the target attribute values.
DecisionStumpAUC
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.33
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.05
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.25
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.32
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.18
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.32
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.18
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.32
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.18
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
67.7
Percentage of instances belonging to the most frequent class.
MajorityClassSize
109
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
21.02
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
54688.74
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
2
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
4.4
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
145427.38
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
9.18
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
1526.55
Mean of means among attributes of the numeric type.
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
2
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
2.49
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
4006.83
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.15
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
0.11
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-1.35
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.11
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
32.3
Percentage of instances belonging to the least frequent class.
MinorityClassSize
52
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.27
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.31
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
1
Number of binary attributes.
PercentageOfBinaryFeatures
2.5
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
97.5
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
2.5
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
4.78
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
1.66
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
1.92
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
1.04
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
10.36
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
10.71
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
3
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
15.14
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
13.26
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
34.92
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
3.39
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
40.95
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.3
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.08
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.3
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.08
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.3
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.08
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.57
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.35
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.14
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.57
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.35
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.14
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.57
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.35
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.14
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
0
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.29
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.28
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

15 tasks

Supervised Classification on mc2
578 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: c
Supervised Classification on mc2
194 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: c
Supervised Classification on mc2
0 runs - estimation_procedure: 33% Holdout set - target_feature: c
Supervised Data Stream Classification on mc2
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: c
Clustering on mc2
0 runs
Clustering on mc2
0 runs - estimation_procedure: 50 times Clustering
Clustering on mc2
0 runs - estimation_procedure: 50 times Clustering
Clustering on mc2
0 runs - estimation_procedure: 50 times Clustering
Clustering on mc2
0 runs - estimation_procedure: 50 times Clustering
Clustering on mc2
0 runs - estimation_procedure: 50 times Clustering
Clustering on mc2
0 runs - estimation_procedure: 50 times Clustering
Clustering on mc2
0 runs - estimation_procedure: 50 times Clustering
Clustering on mc2
0 runs - estimation_procedure: 50 times Clustering
Clustering on mc2
0 runs - estimation_procedure: 50 times Clustering
Clustering on mc2
0 runs - estimation_procedure: 50 times Clustering
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