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datatrieve

datatrieve

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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, 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: The transition of the DATATRIEVE product from version 6.0 to version 6.1 2. Sources: -- Creators: DATATRIEVETM project carried out at Digital Engineering Italy -- Donor: Guenther Ruhe -- Date: January 15, 2005 3. Past usage: A hybrid approach to analyze empirical software engineering data and its application to predict module fault-proneness in maintenance Source Journal of Systems and Software archive Volume 53 , Issue 3 (September 2000) table of contents Pages: 225 - 237 Year of Publication: 2000 ISSN:0164-1212 Authors Sandro Morasca Gunther Ruhe 4. Relevant information: The DATATRIEVE product was undergoing both adaptive (DATATRIEVE was being transferred from platform OpenVMS/VAX to platform OpenVMS/Alpha) and corrective maintenance (failures reported from customers were being fixed) at the Gallarate (Italy) site of Digital Engineering. The DATATRIEVE product was originally developed in the BLISS language. BLISS is an expression language. It is block-structured, with exception handling facilities, coroutines, and a macro system. It was one of the first non-assembly languages for operating system implementation.. Some parts were later added or rewritten in the C language. Therefore, the overall structure of DATATRIEVE is composed of C functions and BLISS subroutines. The empirical study of this data set reports only the BLISS part, by far the bigger one. In what follows, we will use the term "module" to refer to a BLISS module, i.e., a set of declarations and subroutines usually belonging to one file. More than 100 BLISS modules have been studied. It was important to the DATATRIEVE team to better understand how the characteristics of the modules and transition process were correlated with the code quality. The objective of the data analysis was to study whether it was possible to classify modules as non-faulty or faulty, based on a set of measures collected on the project. 5. Number of records: 130 6. Number of attributes: 9 8 condition attributes 1 decision attribute 7. Attribute Information: 1. LOC6_0: number of lines of code of module m in version 6.0. 2. LOC6_1: number of lines of code of module m in version 6.1. 3. AddedLOC: number of lines of code that were added to module m in version 6.1, i.e., they were not present in module m in version 6.0. 4. DeletedLOC: number of lines of code that were deleted from module m in version 6.0, i.e., they were no longer present in module m in version 6.1. 5. DifferentBlocks: number of different blocks module m in between versions 6.0 and 6.1. 6. ModificationRate: rate of modification of module m, i.e., (AddedLOC + DeletedLOC) / (LOC6.0 + AddedLOC). 7. ModuleKnowledge: subjective variable that expresses the project team's knowledge on module m (low or high) 8. ReusedLOC: number of lines of code of module m in version 6.0 reused in module m in version 6.1. 9. Faulty6_1: its value is 0 for all those modules in which no faults were found; its value is 1 for all other modules. 8. Missing attributes: none 9. Class Distribution: 0: 119 = 91.54% 1: 11 = 8.46% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

9 features

Faulty6_1 (target)nominal2 unique values
0 missing
LOC6_0numeric125 unique values
0 missing
LOC6_1numeric123 unique values
0 missing
Added_LoCnumeric103 unique values
0 missing
Del_LoCnumeric98 unique values
0 missing
Diff_Blocknumeric58 unique values
0 missing
Mod_Ratenumeric47 unique values
0 missing
Mod_Knownumeric2 unique values
0 missing
ReusedLoCnumeric122 unique values
0 missing
Show all 9 features

107 properties

NumberOfInstances
130
Number of instances (rows) of the dataset.
NumberOfFeatures
9
Number of attributes (columns) of the dataset.
NumberOfClasses
2
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
8
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
AutoCorrelation
0.95
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
0.59
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
CfsSubsetEval_DecisionStumpErrRate
0.08
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
CfsSubsetEval_DecisionStumpKappa
0.13
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
CfsSubsetEval_NaiveBayesAUC
0.59
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
CfsSubsetEval_NaiveBayesErrRate
0.08
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
CfsSubsetEval_NaiveBayesKappa
0.13
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
CfsSubsetEval_kNN1NAUC
0.59
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
CfsSubsetEval_kNN1NErrRate
0.08
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
CfsSubsetEval_kNN1NKappa
0.13
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
ClassEntropy
0.42
Entropy of the target attribute values.
DecisionStumpAUC
0.65
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.09
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
-0.01
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.07
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
0.59
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.08
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.13
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.59
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.08
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.13
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.59
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.08
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.13
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
91.54
Percentage of instances belonging to the most frequent class.
MajorityClassSize
119
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
6.88
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
902.87
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
2
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
2.06
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
838.74
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
3.49
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
357.79
Mean of means among attributes of the numeric type.
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
2
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
1.53
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
336.91
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-2.01
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
1.46
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
0.16
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.5
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
8.46
Percentage of instances belonging to the least frequent class.
MinorityClassSize
11
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.59
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.2
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.14
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
1
Number of binary attributes.
PercentageOfBinaryFeatures
11.11
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
88.89
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
11.11
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
1.57
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
24.7
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
1.36
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
15.33
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
3.67
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
114.05
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
1.71
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
114.79
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
6.18
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
867.5
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
1.94
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
815.84
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.52
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.09
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
-0.01
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.52
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.09
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
-0.01
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.52
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.09
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
-0.01
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.13
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.25
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.13
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.25
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.13
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.25
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
0
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.57
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.1
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

14 tasks

Supervised Classification on datatrieve
699 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Faulty6_1
Supervised Classification on datatrieve
209 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Faulty6_1
Supervised Data Stream Classification on datatrieve
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: Faulty6_1
Clustering on datatrieve
0 runs
Clustering on datatrieve
0 runs - estimation_procedure: 50 times Clustering
Clustering on datatrieve
0 runs - estimation_procedure: 50 times Clustering
Clustering on datatrieve
0 runs - estimation_procedure: 50 times Clustering
Clustering on datatrieve
0 runs - estimation_procedure: 50 times Clustering
Clustering on datatrieve
0 runs - estimation_procedure: 50 times Clustering
Clustering on datatrieve
0 runs - estimation_procedure: 50 times Clustering
Clustering on datatrieve
0 runs - estimation_procedure: 50 times Clustering
Clustering on datatrieve
0 runs - estimation_procedure: 50 times Clustering
Clustering on datatrieve
0 runs - estimation_procedure: 50 times Clustering
Clustering on datatrieve
0 runs - estimation_procedure: 50 times Clustering
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