Data
pc3

pc3

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: PC3/software 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
HALSTEAD_ERROR_ESTnumeric139 unique values
0 missing
HALSTEAD_EFFORTnumeric1329 unique values
0 missing
HALSTEAD_LENGTHnumeric357 unique values
0 missing
HALSTEAD_LEVELnumeric45 unique values
0 missing
HALSTEAD_PROG_TIMEnumeric1318 unique values
0 missing
HALSTEAD_VOLUMEnumeric1055 unique values
0 missing
MAINTENANCE_SEVERITYnumeric81 unique values
0 missing
MODIFIED_CONDITION_COUNTnumeric50 unique values
0 missing
MULTIPLE_CONDITION_COUNTnumeric68 unique values
0 missing
NODE_COUNTnumeric103 unique values
0 missing
NORMALIZED_CYLOMATIC_COMPLEXITYnumeric68 unique values
0 missing
NUM_OPERANDSnumeric227 unique values
0 missing
NUM_OPERATORSnumeric259 unique values
0 missing
NUM_UNIQUE_OPERANDSnumeric117 unique values
0 missing
NUM_UNIQUE_OPERATORSnumeric43 unique values
0 missing
NUMBER_OF_LINESnumeric170 unique values
0 missing
PERCENT_COMMENTSnumeric377 unique values
0 missing
LOC_TOTALnumeric123 unique values
0 missing
DESIGN_COMPLEXITYnumeric33 unique values
0 missing
BRANCH_COUNTnumeric72 unique values
0 missing
CALL_PAIRSnumeric20 unique values
0 missing
LOC_CODE_AND_COMMENTnumeric25 unique values
0 missing
LOC_COMMENTSnumeric58 unique values
0 missing
CONDITION_COUNTnumeric69 unique values
0 missing
CYCLOMATIC_COMPLEXITYnumeric52 unique values
0 missing
CYCLOMATIC_DENSITYnumeric77 unique values
0 missing
DECISION_COUNTnumeric45 unique values
0 missing
DECISION_DENSITYnumeric52 unique values
0 missing
LOC_BLANKnumeric54 unique values
0 missing
DESIGN_DENSITYnumeric78 unique values
0 missing
EDGE_COUNTnumeric126 unique values
0 missing
ESSENTIAL_COMPLEXITYnumeric25 unique values
0 missing
ESSENTIAL_DENSITYnumeric61 unique values
0 missing
LOC_EXECUTABLEnumeric118 unique values
0 missing
PARAMETER_COUNTnumeric8 unique values
0 missing
HALSTEAD_CONTENTnumeric1174 unique values
0 missing
HALSTEAD_DIFFICULTYnumeric822 unique values
0 missing

107 properties

1563
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.
0.81
Average class difference between consecutive instances.
0.5
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.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0
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
0.5
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
0.1
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
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
0.5
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
0.1
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
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
0.48
Entropy of the target attribute values.
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
0.1
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.02
Number of attributes divided by the number of instances.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.53
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.12
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.12
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.53
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.12
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.12
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.53
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.12
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.12
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
89.76
Percentage of instances belonging to the most frequent class.
1403
Number of instances belonging to the most frequent class.
Maximum entropy among attributes.
1039.34
Maximum kurtosis among attributes of the numeric type.
34072.82
Maximum of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
2
The maximum number of distinct values among attributes of the nominal type.
30.49
Maximum skewness among attributes of the numeric type.
358165.94
Maximum standard deviation of attributes of the numeric type.
Average entropy of the attributes.
238.95
Mean kurtosis among attributes of the numeric type.
1008.15
Mean of means among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
2
Average number of distinct values among the attributes of the nominal type.
10.32
Mean skewness among attributes of the numeric type.
10334.5
Mean standard deviation of attributes of the numeric type.
Minimal entropy among attributes.
-1.48
Minimum kurtosis among attributes of the numeric type.
0.12
Minimum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
-0.59
Minimum skewness among attributes of the numeric type.
0.13
Minimum standard deviation of attributes of the numeric type.
10.24
Percentage of instances belonging to the least frequent class.
160
Number of instances belonging to the least frequent class.
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.48
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.07
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1
Number of binary attributes.
2.63
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
97.37
Percentage of numeric attributes.
2.63
Percentage of nominal attributes.
First quartile of entropy among attributes.
9.98
First quartile of kurtosis among attributes of the numeric type.
1.44
First quartile of means among attributes of the numeric type.
First quartile of mutual information between the nominal attributes and the target attribute.
2.63
First quartile of skewness among attributes of the numeric type.
2.06
First quartile of standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
144.27
Second quartile (Median) of kurtosis among attributes of the numeric type.
7.64
Second quartile (Median) of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
9.78
Second quartile (Median) of skewness among attributes of the numeric type.
15.93
Second quartile (Median) of standard deviation of attributes of the numeric type.
Third quartile of entropy among attributes.
407.27
Third quartile of kurtosis among attributes of the numeric type.
22.68
Third quartile of means among attributes of the numeric type.
Third quartile of mutual information between the nominal attributes and the target attribute.
16.3
Third quartile of skewness among attributes of the numeric type.
43.86
Third quartile of standard deviation of attributes of the numeric type.
0.75
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.trees.REPTree -L 1
0.11
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.trees.REPTree -L 2
0.11
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.11
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.59
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.16
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.59
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.16
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.59
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.16
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.17
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0
Standard deviation of the number of distinct values among attributes of the nominal type.
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.12
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
0.26
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk

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: 10% Holdout set - 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-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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