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kc3

kc3

active ARFF Publicly available Visibility: public Uploaded 06-10-2014 by Joaquin Vanschoren
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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
LOC_BLANKnumeric25 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
DESIGN_COMPLEXITYnumeric21 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
HALSTEAD_DIFFICULTYnumeric223 unique values
0 missing
HALSTEAD_EFFORTnumeric297 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

107 properties

458
Number of instances (rows) of the dataset.
40
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.
39
Number of numeric attributes.
1
Number of nominal attributes.
0.55
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.09
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
44394.87
Maximum standard deviation of attributes of the numeric type.
9.39
Percentage of instances belonging to the least frequent class.
97.5
Percentage of numeric attributes.
17.14
Third quartile of means among attributes of the numeric type.
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
0.13
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.31
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
43
Number of instances belonging to the least frequent class.
2.5
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
0.15
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
0.12
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
18.93
Mean kurtosis among attributes of the numeric type.
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
3.78
Third quartile of skewness among attributes of the numeric type.
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
0.55
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.09
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
395.49
Mean of means among attributes of the numeric type.
0.15
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
6.53
First quartile of kurtosis among attributes of the numeric type.
26.47
Third quartile of standard deviation of attributes of the numeric type.
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
0.13
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.31
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0.28
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.93
First quartile of means among attributes of the numeric type.
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
0.12
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
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.
0.09
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
0
Standard deviation of the number of distinct values among attributes of the nominal type.
0.09
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
2
Average number of distinct values among the attributes of the nominal type.
2.44
First quartile of skewness among attributes of the numeric type.
0.68
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
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.31
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
3.1
Mean skewness among attributes of the numeric type.
0.86
First quartile of standard deviation of attributes of the numeric type.
0.09
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
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
0.11
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
90.61
Percentage of instances belonging to the most frequent class.
1244.99
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.45
Entropy of the target attribute values.
0.28
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
415
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
11.39
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.74
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
-1.5
Minimum kurtosis among attributes of the numeric type.
3.45
Second quartile (Median) of means among attributes of the numeric type.
0.09
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.09
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
135.62
Maximum kurtosis among attributes of the numeric type.
0.1
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
13728.24
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
3.06
Second quartile (Median) of skewness among attributes of the numeric type.
0.55
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.09
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
2.5
Percentage of binary attributes.
5.8
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.13
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.
-2.02
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.12
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
10.03
Maximum skewness among attributes of the numeric type.
0.13
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
20.6
Third quartile of kurtosis among attributes of the numeric type.
0.83
Average class difference between consecutive instances.

7 tasks

564 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: c
213 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: c
0 runs - estimation_procedure: 33% Holdout set - target_feature: c
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: c
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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