Data
pc4

pc4

active ARFF Publicly available Visibility: public Uploaded 06-10-2014 by Joaquin Vanschoren
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Author: Source: Unknown - Date unknown Please cite: %-*- text -*- %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% This is a PROMISE data set made publicly available in order to encourage repeatable, verifiable, refutable, and/or improvable predictive models of software engineering. If you publish material based on PROMISE data sets then, please follow the acknowledgment guidelines posted on the PROMISE repository web page http://promise.site.uottawa.ca/SERepository . %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 1. Title/Topic: PC4/ defect prediction (c) 2007 : Tim Menzies : tim@menzies.us This data set is distributed under the Creative Commons Attribution-Share Alike 3.0 License http://creativecommons.org/licenses/by-sa/3.0/ You are free: * to Share -- copy, distribute and transmit the work * to Remix -- to adapt the work Under the following conditions: Attribution. You must attribute the work in the manner specified by the author or licensor (but not in any way that suggests that they endorse you or your use of the work). Share Alike. If you alter, transform, or build upon this work, you may distribute the resulting work only under the same, similar or a compatible license. * For any reuse or distribution, you must make clear to others the license terms of this work. * Any of the above conditions can be waived if you get permission from the copyright holder. * Apart from the remix rights granted under this license, nothing in this license impairs or restricts the author's moral rights. For more deatils on this data set, see http://promisedata.org/repository/data/kc2/kc2.arff

38 features

c (target)nominal2 unique values
0 missing
LOC_BLANKnumeric54 unique values
0 missing
BRANCH_COUNTnumeric61 unique values
0 missing
CALL_PAIRSnumeric22 unique values
0 missing
LOC_CODE_AND_COMMENTnumeric36 unique values
0 missing
LOC_COMMENTSnumeric57 unique values
0 missing
CONDITION_COUNTnumeric41 unique values
0 missing
CYCLOMATIC_COMPLEXITYnumeric43 unique values
0 missing
CYCLOMATIC_DENSITYnumeric70 unique values
0 missing
DECISION_COUNTnumeric23 unique values
0 missing
DECISION_DENSITYnumeric5 unique values
0 missing
DESIGN_COMPLEXITYnumeric31 unique values
0 missing
DESIGN_DENSITYnumeric76 unique values
0 missing
EDGE_COUNTnumeric105 unique values
0 missing
ESSENTIAL_COMPLEXITYnumeric25 unique values
0 missing
ESSENTIAL_DENSITYnumeric2 unique values
0 missing
LOC_EXECUTABLEnumeric107 unique values
0 missing
PARAMETER_COUNTnumeric8 unique values
0 missing
HALSTEAD_CONTENTnumeric1021 unique values
0 missing
HALSTEAD_DIFFICULTYnumeric708 unique values
0 missing
HALSTEAD_EFFORTnumeric1165 unique values
0 missing
HALSTEAD_ERROR_ESTnumeric120 unique values
0 missing
HALSTEAD_LENGTHnumeric336 unique values
0 missing
HALSTEAD_LEVELnumeric40 unique values
0 missing
HALSTEAD_PROG_TIMEnumeric1159 unique values
0 missing
HALSTEAD_VOLUMEnumeric941 unique values
0 missing
MAINTENANCE_SEVERITYnumeric74 unique values
0 missing
MODIFIED_CONDITION_COUNTnumeric28 unique values
0 missing
MULTIPLE_CONDITION_COUNTnumeric40 unique values
0 missing
NODE_COUNTnumeric89 unique values
0 missing
NORMALIZED_CYLOMATIC_COMPLEXITYnumeric67 unique values
0 missing
NUM_OPERANDSnumeric184 unique values
0 missing
NUM_OPERATORSnumeric245 unique values
0 missing
NUM_UNIQUE_OPERANDSnumeric71 unique values
0 missing
NUM_UNIQUE_OPERATORSnumeric38 unique values
0 missing
NUMBER_OF_LINESnumeric171 unique values
0 missing
PERCENT_COMMENTSnumeric394 unique values
0 missing
LOC_TOTALnumeric116 unique values
0 missing

107 properties

1458
Number of instances (rows) of the dataset.
38
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.
37
Number of numeric attributes.
1
Number of nominal attributes.
48.05
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.35
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.54
Entropy of the target attribute values.
0.32
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
1280
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
7
Second quartile (Median) of means among attributes of the numeric type.
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
-1.51
Minimum kurtosis among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.11
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.12
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
771.63
Maximum kurtosis among attributes of the numeric type.
0.07
Minimum of means among attributes of the numeric type.
5.71
Second quartile (Median) of skewness among attributes of the numeric type.
0.35
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
19505.52
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
9.49
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.03
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.
2.63
Percentage of binary attributes.
Third quartile of entropy among attributes.
0.14
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.
-0.46
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
173.88
Third quartile of kurtosis among attributes of the numeric type.
0.78
Average class difference between consecutive instances.
0.33
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
25.48
Maximum skewness among attributes of the numeric type.
0.12
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
19.71
Third quartile of means among attributes of the numeric type.
0.83
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
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.11
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
62600.26
Maximum standard deviation of attributes of the numeric type.
12.21
Percentage of instances belonging to the least frequent class.
97.37
Percentage of numeric attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.11
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
0.14
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.43
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
178
Number of instances belonging to the least frequent class.
2.63
Percentage of nominal attributes.
9.49
Third quartile of skewness among attributes of the numeric type.
0.3
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
0.33
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
105.09
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.
9.74
First quartile of kurtosis among attributes of the numeric type.
26.37
Third quartile of standard deviation of attributes of the numeric type.
0.83
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
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.11
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
583.46
Mean of means among attributes of the numeric type.
0.17
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.47
First quartile of means among attributes of the numeric type.
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.11
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
0.14
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.43
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0.3
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of mutual information between the nominal attributes and the target attribute.
0.11
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.3
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
0.33
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.71
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.
2.82
First quartile of skewness among attributes of the numeric type.
0.35
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.83
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.
0.11
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.
2.02
First quartile of standard deviation of attributes of the numeric type.
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.11
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
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.43
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
6.45
Mean skewness among attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.11
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.3
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
0.15
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
87.79
Percentage of instances belonging to the most frequent class.
1837.83
Mean standard deviation of attributes of the numeric type.

11 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: c
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: c
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: c
0 runs - estimation_procedure: Leave one out - target_feature: c
0 runs - estimation_procedure: 33% Holdout set - target_feature: c
0 runs - estimation_procedure: Test on Training Data - target_feature: c
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: c
0 runs - estimation_procedure: 10% Holdout set - target_feature: c
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: c
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - target_feature: c
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: c
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