Data
ar6

ar6

active ARFF Publicly available Visibility: public Uploaded 06-10-2014 by Joaquin Vanschoren
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Author: Source: Unknown - Date unknown Please cite: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% This is a PROMISE Software Engineering Repository data set made publicly available in order to encourage repeatable, refutable, verifiable, 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.uottowa.ca/SERepository. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% --Title: AR6 /Software Defect Prediction --Date: February, 4th, 2009 --Data from a Turkish white-goods manufacturer --Donated by: Software Research Laboratory (Softlab), Bogazici University, Istanbul, Turkey --Website: http://softlab.boun.edu.tr --Contact address: ayse.tosun@boun.edu.tr, bener@boun.edu.tr --Description: Embedded software in a white-goods product. Implemented in C. Consists of 101 modules (15 defective / 86 defect-free) 29 static code attributes (McCabe, Halstead and LOC measures) and 1 defect information(false/true) Function/method level static code attributes are collected using Prest Metrics Extraction and Analysis Tool [1]. [1] Prest Metrics Extraction and Analysis Tool, available at http://softlab.boun.edu.tr/?q=resources&i=tools.

30 features

defects (target)nominal2 unique values
0 missing
total_locnumeric43 unique values
0 missing
blank_locnumeric7 unique values
0 missing
comment_locnumeric21 unique values
0 missing
code_and_comment_locnumeric5 unique values
0 missing
executable_locnumeric36 unique values
0 missing
unique_operandsnumeric32 unique values
0 missing
unique_operatorsnumeric19 unique values
0 missing
total_operandsnumeric50 unique values
0 missing
total_operatorsnumeric55 unique values
0 missing
halstead_vocabularynumeric41 unique values
0 missing
halstead_lengthnumeric66 unique values
0 missing
halstead_volumenumeric79 unique values
0 missing
halstead_levelnumeric40 unique values
0 missing
halstead_difficultynumeric39 unique values
0 missing
halstead_effortnumeric87 unique values
0 missing
halstead_errornumeric25 unique values
0 missing
halstead_timenumeric88 unique values
0 missing
branch_countnumeric20 unique values
0 missing
decision_countnumeric19 unique values
0 missing
call_pairsnumeric14 unique values
0 missing
condition_countnumeric17 unique values
0 missing
multiple_condition_countnumeric10 unique values
0 missing
cyclomatic_complexitynumeric15 unique values
0 missing
cyclomatic_densitynumeric34 unique values
0 missing
decision_densitynumeric15 unique values
0 missing
design_complexitynumeric16 unique values
0 missing
design_densitynumeric30 unique values
0 missing
normalized_cyclomatic_complexitynumeric28 unique values
0 missing
formal_parametersnumeric4 unique values
0 missing

108 properties

101
Number of instances (rows) of the dataset.
30
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.
29
Number of numeric attributes.
1
Number of nominal attributes.
0.14
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
-1
Average number of distinct values among the attributes of the nominal type.
1
Number of binary attributes.
NaN
First quartile of mutual information between the nominal attributes and the target attribute.
0.15
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.56
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
-1
Standard deviation of the number of distinct values among attributes of the nominal type.
0.23
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
3.22
Mean skewness among attributes of the numeric type.
0.01
First quartile of skewness among attributes of the numeric type.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.15
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
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.02
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
5569.03
Mean standard deviation of attributes of the numeric type.
0.32
DataQuality extracted from Fantail Library
0.48
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.09
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
0.2
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
85.15
Percentage of instances belonging to the most frequent class.
NaN
Minimal entropy among attributes.
NaN
Second quartile (Median) of entropy among attributes.
0.15
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.61
Entropy of the target attribute values.
0.26
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
86
Number of instances belonging to the most frequent class.
-2
Minimum kurtosis among attributes of the numeric type.
0.35
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.55
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
0.16
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
NaN
Maximum entropy among attributes.
-91638847.15
Minimum of means among attributes of the numeric type.
0.14
Second quartile (Median) of means among attributes of the numeric type.
0.48
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.14
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
14389
Maximum kurtosis among attributes of the numeric type.
NaN
Minimal mutual information between the nominal attributes and the target attribute.
NaN
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.15
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.85
The predictive accuracy obtained by always predicting the majority class.
26074559.21
Maximum of means among attributes of the numeric type.
-1
The minimal number of distinct values among attributes of the nominal type.
0.73
Second quartile (Median) of skewness among attributes of the numeric type.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.3
Number of attributes divided by the number of instances.
NaN
Maximum mutual information between the nominal attributes and the target attribute.
-75.96
Minimum skewness among attributes of the numeric type.
3.33
Percentage of binary attributes.
1
DataQuality extracted from Fantail Library
0.57
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.22
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
NaN
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
-1
The maximum number of distinct values among attributes of the nominal type.
0
DataQuality extracted from Fantail Library
0
Percentage of instances having missing values.
NaN
Third quartile of entropy among attributes.
0.82
Average class difference between consecutive instances.
0.14
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
119.96
Maximum skewness among attributes of the numeric type.
0.15
Percentage of instances belonging to the least frequent class.
0
Percentage of missing values.
14.19
Third quartile of kurtosis among attributes of the numeric type.
0.56
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
0.57
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.23
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
21111483.87
DataQuality extracted from Fantail Library
15
Number of instances belonging to the least frequent class.
96.67
Percentage of numeric attributes.
18.07
Third quartile of means among attributes of the numeric type.
0.15
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
0.22
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.02
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
-1
Average entropy of the attributes.
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
3.33
Percentage of nominal attributes.
NaN
Third quartile of mutual information between the nominal attributes and the target attribute.
0.09
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
0.14
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
81.24
Mean kurtosis among attributes of the numeric type.
0.15
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaN
First quartile of entropy among attributes.
3.62
DataQuality extracted from Fantail Library
0.56
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
0.57
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.23
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
-5253.02
Mean of means among attributes of the numeric type.
0.27
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-1.16
First quartile of kurtosis among attributes of the numeric type.
21.01
DataQuality extracted from Fantail Library
0.15
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
0.22
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.02
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
NaN
Average mutual information between the nominal attributes and the target attribute.
NaN
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0
First quartile of means among attributes of the numeric type.
0.48
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.09
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

4 tasks

549 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: defects
206 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: defects
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: defects
0 runs - estimation_procedure: 50 times Clustering
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