Data
mc1

mc1

active ARFF Publicly available Visibility: public Uploaded 06-10-2014 by Joaquin Vanschoren
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  • mythbusting_1 study_1 study_15 study_20 study_41 study_52 study_7
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Author: Mike Chapman, NASA Source: [tera-PROMISE](http://openscience.us/repo/defect/mccabehalsted/mc1.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. MC1 Software defect prediction One of the NASA Metrics Data Program defect data sets. The specific type of software is unknown. 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. ### 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

39 features

c (target)nominal2 unique values
0 missing
LOC_BLANKnumeric45 unique values
0 missing
BRANCH_COUNTnumeric50 unique values
0 missing
CALL_PAIRSnumeric33 unique values
0 missing
LOC_CODE_AND_COMMENTnumeric35 unique values
0 missing
LOC_COMMENTSnumeric54 unique values
0 missing
CONDITION_COUNTnumeric50 unique values
0 missing
CYCLOMATIC_COMPLEXITYnumeric34 unique values
0 missing
CYCLOMATIC_DENSITYnumeric60 unique values
0 missing
DECISION_COUNTnumeric28 unique values
0 missing
DESIGN_COMPLEXITYnumeric28 unique values
0 missing
DESIGN_DENSITYnumeric2 unique values
0 missing
EDGE_COUNTnumeric103 unique values
0 missing
ESSENTIAL_COMPLEXITYnumeric18 unique values
0 missing
ESSENTIAL_DENSITYnumeric2 unique values
0 missing
LOC_EXECUTABLEnumeric106 unique values
0 missing
PARAMETER_COUNTnumeric11 unique values
0 missing
GLOBAL_DATA_COMPLEXITYnumeric24 unique values
0 missing
GLOBAL_DATA_DENSITYnumeric2 unique values
0 missing
HALSTEAD_CONTENTnumeric1026 unique values
0 missing
HALSTEAD_DIFFICULTYnumeric677 unique values
0 missing
HALSTEAD_EFFORTnumeric1148 unique values
0 missing
HALSTEAD_ERROR_ESTnumeric121 unique values
0 missing
HALSTEAD_LENGTHnumeric290 unique values
0 missing
HALSTEAD_LEVELnumeric50 unique values
0 missing
HALSTEAD_PROG_TIMEnumeric1138 unique values
0 missing
HALSTEAD_VOLUMEnumeric880 unique values
0 missing
MAINTENANCE_SEVERITYnumeric2 unique values
0 missing
MODIFIED_CONDITION_COUNTnumeric32 unique values
0 missing
MULTIPLE_CONDITION_COUNTnumeric50 unique values
0 missing
NODE_COUNTnumeric88 unique values
0 missing
NORMALIZED_CYLOMATIC_COMPLEXITYnumeric48 unique values
0 missing
NUM_OPERANDSnumeric184 unique values
0 missing
NUM_OPERATORSnumeric220 unique values
0 missing
NUM_UNIQUE_OPERANDSnumeric82 unique values
0 missing
NUM_UNIQUE_OPERATORSnumeric46 unique values
0 missing
NUMBER_OF_LINESnumeric147 unique values
0 missing
PERCENT_COMMENTSnumeric322 unique values
0 missing
LOC_TOTALnumeric112 unique values
0 missing

107 properties

9466
Number of instances (rows) of the dataset.
39
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.
38
Number of numeric attributes.
1
Number of nominal attributes.
360.56
Mean of means among attributes of the numeric type.
0.07
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
42.25
First quartile of kurtosis among attributes of the numeric type.
18.21
Third quartile of standard deviation of attributes of the numeric type.
0.64
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
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.83
First quartile of means among attributes of the numeric type.
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.01
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
0.01
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.4
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.
0.01
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.2
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
0.4
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
2
Average number of distinct values among the attributes of the nominal type.
5.34
First quartile of skewness among attributes of the numeric type.
0.2
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.64
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.
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
17.77
Mean skewness among attributes of the numeric type.
1.13
First quartile of standard deviation of attributes of the numeric type.
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.01
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
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.4
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
12709.18
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.01
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.2
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
0.01
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
99.28
Percentage of instances belonging to the most frequent class.
Minimal entropy among attributes.
216.18
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.2
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.06
Entropy of the target attribute values.
0.42
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
9398
Number of instances belonging to the most frequent class.
Maximum entropy among attributes.
-1.96
Minimum kurtosis among attributes of the numeric type.
1.79
Second quartile (Median) of means among attributes of the numeric type.
0.73
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
4585.2
Maximum kurtosis among attributes of the numeric type.
0.01
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.01
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.01
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
12649.5
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
10.25
Second quartile (Median) of skewness among attributes of the numeric type.
0.2
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.
2.56
Percentage of binary attributes.
6.34
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0
Number of attributes divided by the number of instances.
2
The maximum number of distinct values among attributes of the nominal type.
-1.98
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.01
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.
67.26
Maximum skewness among attributes of the numeric type.
0.11
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
1203.14
Third quartile of kurtosis among attributes of the numeric type.
0.99
Average class difference between consecutive instances.
0.4
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
456367.94
Maximum standard deviation of attributes of the numeric type.
0.72
Percentage of instances belonging to the least frequent class.
97.44
Percentage of numeric attributes.
7.26
Third quartile of means among attributes of the numeric type.
0.64
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
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
68
Number of instances belonging to the least frequent class.
2.56
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.01
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
0.01
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.4
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
826.53
Mean kurtosis among attributes of the numeric type.
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
26.89
Third quartile of skewness among attributes of the numeric type.
0.2
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
0.4
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001

9 tasks

549 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: c
196 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: c
0 runs - estimation_procedure: Leave one out - evaluation_measure: matthews_correlation_coefficient - target_feature: c
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: c
70 runs - estimation_procedure: 10-fold Learning Curve - 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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