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kc3

kc3

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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 study_281
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Author: Mike Chapman, NASA Source: [tera-PROMISE](http://openscience.us/repo/defect/mccabehalsted/kc3.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. KC3 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. ### Attribute Information 1. loc : numeric % McCabe's line count of code 2. v(g) : numeric % McCabe "cyclomatic complexity" 3. ev(g) : numeric % McCabe "essential complexity" 4. iv(g) : numeric % McCabe "design complexity" 5. n : numeric % Halstead total operators + operands 6. v : numeric % Halstead "volume" 7. l : numeric % Halstead "program length" 8. d : numeric % Halstead "difficulty" 9. i : numeric % Halstead "intelligence" 10. e : numeric % Halstead "effort" 11. b : numeric % Halstead 12. t : numeric % Halstead's time estimator 13. lOCode : numeric % Halstead's line count 14. lOComment : numeric % Halstead's count of lines of comments 15. lOBlank : numeric % Halstead's count of blank lines 16. lOCodeAndComment: numeric 17. uniq_Op : numeric % unique operators 18. uniq_Opnd : numeric % unique operands 19. total_Op : numeric % total operators 20. total_Opnd : numeric % total operands 21. branchCount : numeric % of the flow graph 22. problems : {false,true} % module has/has not one or more reported defects ### 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_EFFORTnumeric297 unique values
0 missing
HALSTEAD_DIFFICULTYnumeric223 unique values
0 missing
HALSTEAD_ERROR_ESTnumeric77 unique values
0 missing
HALSTEAD_LENGTHnumeric177 unique values
0 missing
HALSTEAD_LEVELnumeric33 unique values
0 missing
HALSTEAD_PROG_TIMEnumeric296 unique values
0 missing
HALSTEAD_VOLUMEnumeric276 unique values
0 missing
MAINTENANCE_SEVERITYnumeric37 unique values
0 missing
MODIFIED_CONDITION_COUNTnumeric18 unique values
0 missing
MULTIPLE_CONDITION_COUNTnumeric29 unique values
0 missing
NODE_COUNTnumeric77 unique values
0 missing
NORMALIZED_CYLOMATIC_COMPLEXITYnumeric34 unique values
0 missing
NUM_OPERANDSnumeric116 unique values
0 missing
NUM_OPERATORSnumeric147 unique values
0 missing
NUM_UNIQUE_OPERANDSnumeric70 unique values
0 missing
NUM_UNIQUE_OPERATORSnumeric29 unique values
0 missing
NUMBER_OF_LINESnumeric89 unique values
0 missing
PERCENT_COMMENTSnumeric87 unique values
0 missing
LOC_TOTALnumeric77 unique values
0 missing
DESIGN_COMPLEXITYnumeric21 unique values
0 missing
BRANCH_COUNTnumeric35 unique values
0 missing
CALL_PAIRSnumeric34 unique values
0 missing
LOC_CODE_AND_COMMENTnumeric6 unique values
0 missing
LOC_COMMENTSnumeric20 unique values
0 missing
CONDITION_COUNTnumeric29 unique values
0 missing
CYCLOMATIC_COMPLEXITYnumeric24 unique values
0 missing
CYCLOMATIC_DENSITYnumeric34 unique values
0 missing
DECISION_COUNTnumeric16 unique values
0 missing
DECISION_DENSITYnumeric22 unique values
0 missing
LOC_BLANKnumeric25 unique values
0 missing
DESIGN_DENSITYnumeric27 unique values
0 missing
EDGE_COUNTnumeric87 unique values
0 missing
ESSENTIAL_COMPLEXITYnumeric13 unique values
0 missing
ESSENTIAL_DENSITYnumeric26 unique values
0 missing
LOC_EXECUTABLEnumeric74 unique values
0 missing
PARAMETER_COUNTnumeric4 unique values
0 missing
GLOBAL_DATA_COMPLEXITYnumeric22 unique values
0 missing
GLOBAL_DATA_DENSITYnumeric31 unique values
0 missing
HALSTEAD_CONTENTnumeric291 unique values
0 missing
Show all 40 features

107 properties

NumberOfInstances
458
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
0.83
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
0.62
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
0.1
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
CfsSubsetEval_DecisionStumpKappa
0.15
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
0.62
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
0.1
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
CfsSubsetEval_NaiveBayesKappa
0.15
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
0.62
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
0.1
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
CfsSubsetEval_kNN1NKappa
0.15
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
0.45
Entropy of the target attribute values.
DecisionStumpAUC
0.74
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.09
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.09
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
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.09
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.31
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.09
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.31
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.09
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.31
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
90.61
Percentage of instances belonging to the most frequent class.
MajorityClassSize
415
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
135.62
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
13728.24
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
10.03
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
44394.87
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
18.93
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
395.49
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
3.1
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
1244.99
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.5
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
0.1
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
-2.02
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.13
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
9.39
Percentage of instances belonging to the least frequent class.
MinorityClassSize
43
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.15
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.28
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
6.53
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
0.93
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
2.44
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
0.86
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
11.39
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
3.45
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
3.06
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
5.8
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
20.6
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
17.14
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
3.78
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
26.47
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.09
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.09
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.09
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.55
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.13
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.12
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.55
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.13
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.12
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.55
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.13
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.12
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
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.11
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 kc3
564 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: c
Supervised Classification on kc3
213 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: c
Supervised Classification on kc3
0 runs - estimation_procedure: 33% Holdout set - target_feature: c
Supervised Data Stream Classification on kc3
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: c
Clustering on kc3
0 runs
Clustering on kc3
0 runs - estimation_procedure: 50 times Clustering
Clustering on kc3
0 runs - estimation_procedure: 50 times Clustering
Clustering on kc3
0 runs - estimation_procedure: 50 times Clustering
Clustering on kc3
0 runs - estimation_procedure: 50 times Clustering
Clustering on kc3
0 runs - estimation_procedure: 50 times Clustering
Clustering on kc3
0 runs - estimation_procedure: 50 times Clustering
Clustering on kc3
0 runs - estimation_procedure: 50 times Clustering
Clustering on kc3
0 runs - estimation_procedure: 50 times Clustering
Clustering on kc3
0 runs - estimation_procedure: 50 times Clustering
Clustering on kc3
0 runs - estimation_procedure: 50 times Clustering
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