DEVELOPMENT... OpenML
Data
kc1

kc1

active ARFF Publicly available Visibility: public Uploaded 06-10-2014 by Felicia West
2 likes downloaded by 29 people , 31 total downloads 0 issues 0 downvotes
  • mythbusting_1 OpenML-CC18 OpenML100 PROMISE study_1 study_123 study_14 study_15 study_20 study_218 study_34 study_41 study_52 study_7 study_98 study_99 study_225 study_271 study_240 study_253 study_379
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Author: Mike Chapman, NASA Source: [tera-PROMISE](http://openscience.us/repo/defect/mccabehalsted/kc1.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. KC1 Software defect prediction One of the NASA Metrics Data Program defect data sets. Data from software for storage management for receiving and processing ground data. 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

22 features

defects (target)nominal2 unique values
0 missing
tnumeric947 unique values
0 missing
branchCountnumeric44 unique values
0 missing
total_Opndnumeric153 unique values
0 missing
total_Opnumeric207 unique values
0 missing
uniq_Opndnumeric73 unique values
0 missing
uniq_Opnumeric34 unique values
0 missing
locCodeAndCommentnumeric12 unique values
0 missing
lOBlanknumeric31 unique values
0 missing
lOCommentnumeric28 unique values
0 missing
lOCodenumeric121 unique values
0 missing
locnumeric139 unique values
0 missing
bnumeric92 unique values
0 missing
enumeric961 unique values
0 missing
inumeric893 unique values
0 missing
dnumeric548 unique values
0 missing
lnumeric52 unique values
0 missing
vnumeric729 unique values
0 missing
nnumeric278 unique values
0 missing
iv(g)numeric26 unique values
0 missing
ev(g)numeric21 unique values
0 missing
v(g)numeric31 unique values
0 missing
Show all 22 features

107 properties

NumberOfInstances
2109
Number of instances (rows) of the dataset.
NumberOfFeatures
22
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
21
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
AutoCorrelation
1
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
0.71
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
0.16
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
0.26
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
0.71
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
0.16
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
0.26
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
0.71
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
0.16
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
0.26
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
0.62
Entropy of the target attribute values.
DecisionStumpAUC
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.15
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.01
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
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.15
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.22
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.15
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.22
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.15
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.22
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
84.54
Percentage of instances belonging to the most frequent class.
MajorityClassSize
1783
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
103.08
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
5242.39
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
8.79
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
17444.98
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
31.85
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
285.1
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
4.2
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
915.46
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
1.24
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
0.09
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
1.14
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.18
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
15.46
Percentage of instances belonging to the least frequent class.
MinorityClassSize
326
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.17
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.3
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
1
Number of binary attributes.
PercentageOfBinaryFeatures
4.55
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
95.45
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
4.55
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
12.28
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
1.72
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
2.87
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
3.23
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
22.42
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
7.63
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
3.74
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
7.86
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
36.53
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
26.14
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
4.71
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
41.93
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.14
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.18
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.14
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.18
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.14
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.18
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.18
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.31
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.18
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.31
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.18
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.31
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
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.15
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.35
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

30 tasks

Supervised Classification on kc1
158595 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: defects
Supervised Classification on kc1
213 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: defects
Supervised Classification on kc1
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: matthews_correlation_coefficient - target_feature: defects
Supervised Classification on kc1
0 runs - estimation_procedure: 33% Holdout set - target_feature: defects
Supervised Classification on kc1
0 runs - estimation_procedure: 20% Holdout (Ordered) - evaluation_measure: predictive_accuracy - target_feature: defects
Supervised Classification on kc1
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: defects
Supervised Classification on kc1
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: defects
Supervised Classification on kc1
0 runs - estimation_procedure: 4-fold Crossvalidation - target_feature: defects
Learning Curve on kc1
85 runs - estimation_procedure: 10-fold Learning Curve - target_feature: defects
Supervised Data Stream Classification on kc1
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: defects
Clustering on kc1
0 runs - target_feature: defects
Clustering on kc1
0 runs
Clustering on kc1
0 runs - estimation_procedure: 50 times Clustering
Clustering on kc1
0 runs - estimation_procedure: 50 times Clustering
Clustering on kc1
0 runs - estimation_procedure: 50 times Clustering
Clustering on kc1
0 runs - estimation_procedure: 50 times Clustering
Clustering on kc1
0 runs - estimation_procedure: 50 times Clustering
Clustering on kc1
0 runs - estimation_procedure: 50 times Clustering
Clustering on kc1
0 runs - estimation_procedure: 50 times Clustering
Clustering on kc1
0 runs - estimation_procedure: 50 times Clustering
Clustering on kc1
0 runs - estimation_procedure: 50 times Clustering
Clustering on kc1
0 runs - estimation_procedure: 50 times Clustering
Subgroup Discovery on kc1
1312 runs - target_feature: defects
Subgroup Discovery on kc1
1311 runs - target_feature: defects
Subgroup Discovery on kc1
0 runs - target_feature: defects
Subgroup Discovery on kc1
0 runs - target_feature: defects
Subgroup Discovery on kc1
0 runs - target_feature: defects
Subgroup Discovery on kc1
0 runs - target_feature: defects
Subgroup Discovery on kc1
0 runs - target_feature: defects
Subgroup Discovery on kc1
0 runs - target_feature: defects
Define a new task

A PHP Error was encountered

Severity: Core Warning

Message: Module 'mysqli' already loaded

Filename: Unknown

Line Number: 0

Backtrace: