Data
mw1

mw1

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: MW1/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
LOC_BLANKnumeric27 unique values
0 missing
BRANCH_COUNTnumeric33 unique values
0 missing
CALL_PAIRSnumeric24 unique values
0 missing
LOC_CODE_AND_COMMENTnumeric8 unique values
0 missing
LOC_COMMENTSnumeric27 unique values
0 missing
CONDITION_COUNTnumeric31 unique values
0 missing
CYCLOMATIC_COMPLEXITYnumeric25 unique values
0 missing
CYCLOMATIC_DENSITYnumeric53 unique values
0 missing
DECISION_COUNTnumeric18 unique values
0 missing
DECISION_DENSITYnumeric29 unique values
0 missing
DESIGN_COMPLEXITYnumeric20 unique values
0 missing
DESIGN_DENSITYnumeric39 unique values
0 missing
EDGE_COUNTnumeric73 unique values
0 missing
ESSENTIAL_COMPLEXITYnumeric16 unique values
0 missing
ESSENTIAL_DENSITYnumeric2 unique values
0 missing
LOC_EXECUTABLEnumeric71 unique values
0 missing
PARAMETER_COUNTnumeric7 unique values
0 missing
HALSTEAD_CONTENTnumeric354 unique values
0 missing
HALSTEAD_DIFFICULTYnumeric278 unique values
0 missing
HALSTEAD_EFFORTnumeric363 unique values
0 missing
HALSTEAD_ERROR_ESTnumeric63 unique values
0 missing
HALSTEAD_LENGTHnumeric163 unique values
0 missing
HALSTEAD_LEVELnumeric43 unique values
0 missing
HALSTEAD_PROG_TIMEnumeric361 unique values
0 missing
HALSTEAD_VOLUMEnumeric332 unique values
0 missing
MAINTENANCE_SEVERITYnumeric42 unique values
0 missing
MODIFIED_CONDITION_COUNTnumeric21 unique values
0 missing
MULTIPLE_CONDITION_COUNTnumeric32 unique values
0 missing
NODE_COUNTnumeric60 unique values
0 missing
NORMALIZED_CYLOMATIC_COMPLEXITYnumeric45 unique values
0 missing
NUM_OPERANDSnumeric106 unique values
0 missing
NUM_OPERATORSnumeric122 unique values
0 missing
NUM_UNIQUE_OPERANDSnumeric64 unique values
0 missing
NUM_UNIQUE_OPERATORSnumeric31 unique values
0 missing
NUMBER_OF_LINESnumeric92 unique values
0 missing
PERCENT_COMMENTSnumeric151 unique values
0 missing
LOC_TOTALnumeric70 unique values
0 missing

107 properties

403
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.
92.31
Percentage of instances belonging to the most frequent class.
556.77
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.08
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.16
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
0.1
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
0.23
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
372
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
6.09
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.39
Entropy of the target attribute values.
Maximum entropy among attributes.
-1.53
Minimum kurtosis among attributes of the numeric type.
4.94
Second quartile (Median) of means among attributes of the numeric type.
0.49
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.65
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
57.15
Maximum kurtosis among attributes of the numeric type.
0.05
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.08
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.08
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
7985.33
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
2.21
Second quartile (Median) of skewness among attributes of the numeric type.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
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.
6.44
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.56
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.
2
The maximum number of distinct values among attributes of the nominal type.
-1.27
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
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.
7.3
Maximum skewness among attributes of the numeric type.
0.09
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
16.68
Third quartile of kurtosis among attributes of the numeric type.
0.86
Average class difference between consecutive instances.
0.08
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.53
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
18556.57
Maximum standard deviation of attributes of the numeric type.
7.69
Percentage of instances belonging to the least frequent class.
97.37
Percentage of numeric attributes.
20.63
Third quartile of means among attributes of the numeric type.
0.63
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
0.56
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.08
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
31
Number of instances belonging to the least frequent class.
2.63
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.09
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
0.13
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.26
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
11.65
Mean kurtosis among attributes of the numeric type.
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
3.24
Third quartile of skewness among attributes of the numeric type.
0.16
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
0.08
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.53
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
251.89
Mean of means among attributes of the numeric type.
0.16
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
3.89
First quartile of kurtosis among attributes of the numeric type.
19.27
Third quartile of standard deviation of attributes of the numeric type.
0.63
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
0.56
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.08
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0.26
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.18
First quartile of means among attributes of the numeric type.
0.49
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.09
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
0.26
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
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.
0.08
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.16
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
0.08
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.53
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.08
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.
1.71
First quartile of skewness among attributes of the numeric type.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.63
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.
0.26
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
2.52
Mean skewness among attributes of the numeric type.
1.58
First quartile of standard deviation of attributes of the numeric type.
0.49
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.09
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
0.63
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk

7 tasks

559 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: c
206 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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