Data
kc2

kc2

active ARFF Publicly available Visibility: public Uploaded 06-10-2014 by Joaquin Vanschoren
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  • OpenML-CC18 OpenML100 study_1 study_123 study_135 study_14 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/kc2.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. KC2 Software defect prediction One of the NASA Metrics Data Program defect data sets. Data from software for science data processing. 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

problems (target)nominal2 unique values
0 missing
locnumeric123 unique values
0 missing
v(g)numeric37 unique values
0 missing
ev(g)numeric21 unique values
0 missing
iv(g)numeric26 unique values
0 missing
nnumeric196 unique values
0 missing
vnumeric297 unique values
0 missing
lnumeric44 unique values
0 missing
dnumeric254 unique values
0 missing
inumeric319 unique values
0 missing
enumeric329 unique values
0 missing
bnumeric86 unique values
0 missing
tnumeric330 unique values
0 missing
lOCodenumeric112 unique values
0 missing
lOCommentnumeric28 unique values
0 missing
lOBlanknumeric35 unique values
0 missing
lOCodeAndCommentnumeric8 unique values
0 missing
uniq_Opnumeric30 unique values
0 missing
uniq_Opndnumeric66 unique values
0 missing
total_Opnumeric152 unique values
0 missing
total_Opndnumeric131 unique values
0 missing
branchCountnumeric37 unique values
0 missing

107 properties

522
Number of instances (rows) of the dataset.
22
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.
21
Number of numeric attributes.
1
Number of nominal attributes.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.16
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.2
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
321.26
Maximum kurtosis among attributes of the numeric type.
0.19
Minimum of means among attributes of the numeric type.
10.08
Second quartile (Median) of skewness among attributes of the numeric type.
0.41
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.4
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
18542.99
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
4.55
Percentage of binary attributes.
21.94
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.04
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.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.19
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.
1.05
Minimum skewness among attributes of the numeric type.
0
Percentage of missing values.
199.21
Third quartile of kurtosis among attributes of the numeric type.
0.99
Average class difference between consecutive instances.
0.38
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
16.79
Maximum skewness among attributes of the numeric type.
0.28
Minimum standard deviation of attributes of the numeric type.
95.45
Percentage of numeric attributes.
47.32
Third quartile of means among attributes of the numeric type.
0.73
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
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.18
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
113271.22
Maximum standard deviation of attributes of the numeric type.
20.5
Percentage of instances belonging to the least frequent class.
4.55
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.18
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
0.19
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.33
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
107
Number of instances belonging to the least frequent class.
First quartile of entropy among attributes.
12.28
Third quartile of skewness among attributes of the numeric type.
0.34
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
0.38
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
130.11
Mean kurtosis among attributes of the numeric type.
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
43.98
First quartile of kurtosis among attributes of the numeric type.
116.69
Third quartile of standard deviation of attributes of the numeric type.
0.73
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
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.18
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
977.89
Mean of means among attributes of the numeric type.
0.17
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
3.05
First quartile of means among attributes of the numeric type.
0.78
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.18
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
0.19
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.33
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0.41
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of mutual information between the nominal attributes and the target attribute.
0.16
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.34
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
0.38
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.73
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.
5.48
First quartile of skewness among attributes of the numeric type.
0.41
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.73
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.
0.18
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.
6.51
First quartile of standard deviation of attributes of the numeric type.
0.78
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.18
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
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.33
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
8.95
Mean skewness among attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.16
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.34
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
0.2
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
79.5
Percentage of instances belonging to the most frequent class.
5875.73
Mean standard deviation of attributes of the numeric type.
139.55
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.41
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.73
Entropy of the target attribute values.
0.39
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
415
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
9.74
Second quartile (Median) of means among attributes of the numeric type.
0.78
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
1.99
Minimum kurtosis among attributes of the numeric type.

17 tasks

165151 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: problems
214 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: problems
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: problems
0 runs - estimation_procedure: 33% Holdout set - target_feature: problems
46 runs - estimation_procedure: 10-fold Learning Curve - target_feature: problems
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: problems
0 runs - target_feature: problems
1312 runs - target_feature: problems
1307 runs - target_feature: problems
0 runs - target_feature: problems
0 runs - target_feature: problems
0 runs - target_feature: problems
0 runs - target_feature: problems
0 runs - target_feature: problems
0 runs - target_feature: problems
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