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pc1

pc1

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  • Chemistry Life Science mythbusting_1 OpenML-CC18 OpenML100 PROMISE study_1 study_123 study_14 study_15 study_20 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/pc1.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. PC1 Software defect prediction One of the NASA Metrics Data Program defect data sets. Data from flight software for earth orbiting satellite. 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. branchCount : numeric % of the flow graph 23. defects : {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
locnumeric111 unique values
0 missing
v(g)numeric48 unique values
0 missing
ev(g)numeric27 unique values
0 missing
iv(G)numeric31 unique values
0 missing
Nnumeric312 unique values
0 missing
Vnumeric756 unique values
0 missing
Lnumeric45 unique values
0 missing
Dnumeric613 unique values
0 missing
Inumeric823 unique values
0 missing
Enumeric890 unique values
0 missing
Bnumeric126 unique values
0 missing
Tnumeric886 unique values
0 missing
lOCodenumeric113 unique values
0 missing
lOCommentnumeric53 unique values
0 missing
locCodeAndCommentnumeric24 unique values
0 missing
lOBlanknumeric52 unique values
0 missing
uniq_Opnumeric46 unique values
0 missing
uniq_Opndnumeric106 unique values
0 missing
total_Opnumeric232 unique values
0 missing
total_Opndnumeric203 unique values
0 missing
branchCountnumeric62 unique values
0 missing

107 properties

1109
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.
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.02
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.
4.55
Percentage of binary attributes.
16.54
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.1
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.
2.17
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.21
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.57
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
18.55
Maximum skewness among attributes of the numeric type.
0.15
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
113.16
Third quartile of kurtosis among attributes of the numeric type.
1
Average class difference between consecutive instances.
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.07
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
170643.6
Maximum standard deviation of attributes of the numeric type.
6.94
Percentage of instances belonging to the least frequent class.
95.45
Percentage of numeric attributes.
58.7
Third quartile of means among attributes of the numeric type.
0.75
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
0.1
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.21
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
77
Number 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.
0.07
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
0.11
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
0.21
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.57
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
116.05
Mean kurtosis among attributes of the numeric type.
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
8.42
Third quartile of skewness among attributes of the numeric type.
0.75
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
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.07
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
1500.99
Mean of means among attributes of the numeric type.
0.11
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
56.29
First quartile of kurtosis among attributes of the numeric type.
99.01
Third quartile of standard deviation of attributes of the numeric type.
0.07
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
0.1
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.21
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0.21
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
4.01
First quartile of means among attributes of the numeric type.
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.11
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
0.21
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.57
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.
0.07
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.03
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.75
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.
0.07
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.
5.65
First quartile of skewness among attributes of the numeric type.
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.07
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
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.21
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
8.01
Mean skewness among attributes of the numeric type.
7.29
First quartile of standard deviation of attributes of the numeric type.
0.07
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.11
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
0.07
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
93.06
Percentage of instances belonging to the most frequent class.
8678.61
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.03
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.36
Entropy of the target attribute values.
0.41
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
1032
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
90.72
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.69
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.
13.73
Minimum kurtosis among attributes of the numeric type.
15.4
Second quartile (Median) of means among attributes of the numeric type.
0.07
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.07
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
410.49
Maximum kurtosis among attributes of the numeric type.
0.13
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.03
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
28822.88
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
7.43
Second quartile (Median) of skewness among attributes of the numeric type.

28 tasks

147053 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: defects
235 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: defects
0 runs - estimation_procedure: 33% Holdout set - target_feature: defects
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: defects
0 runs - estimation_procedure: 4-fold Crossvalidation - target_feature: defects
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: area_under_roc_curve - target_feature: defects
86 runs - estimation_procedure: 10-fold Learning Curve - target_feature: defects
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: defects
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - target_feature: defects
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
1316 runs - target_feature: defects
1308 runs - target_feature: defects
0 runs - target_feature: defects
0 runs - target_feature: defects
0 runs - target_feature: defects
0 runs - target_feature: defects
0 runs - target_feature: defects
0 runs - target_feature: defects
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