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mozilla4

mozilla4

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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://promisedata.org/repository . %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% (c) 2007 A. Gunes Koru Contact: gkoru AT umbc DOT edu Phone: +1 (410) 455 8843 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. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 1. Title: Recurrent event (defect fix) and size data for Mozilla Classes This one includes a binary attribute (event) to show defect fix. The data is at the "observation" level. Each modification made to a C++ class was entered as an observation. A newly added class created an observation. The observation period was between May 29, 2002 and Feb 22, 2006. 2. Sources (a) Creator: A. Gunes Koru (b) Date: February 23, 2007 (c) Contact: gkoru AT umbc DOT edu Phone: +1 (410) 455 8843 3. Donor: A. Gunes Koru 4. Past Usage: This data set was used for: A. Gunes Koru, Dongsong Zhang, and Hongfang Liu, "Modeling the Effect of Size on Defect Proneness for Open-Source Software", Predictive Models in Software Engineering Workshop, PROMISE 2007, May 20th 2007, Minneapolis, Minnesota, US. Abstract: Quality is becoming increasingly important with the continuous adoption of open-source software. Previous research has found that there is generally a positive relationship between module size and defect proneness. Therefore, in open-source software development, it is important to monitor module size and understand its impact on defect proneness. However, traditional approaches to quality modeling, which measure specific system snapshots and obtain future defect counts, are not well suited because open-source modules usually evolve and their size changes over time. In this study, we used Cox proportional hazards modeling with recurrent events to study the effect of class size on defect-proneness in the Mozilla product. We found that the effect of size was significant, and we quantified this effect on defect proneness. The full paper can be downloaded from A. Gunes Koru's Website http://umbc.edu/~gkoru by following the Publications link or from the Web site of PROMISE 2007. 5. Features: This data set is used to create a conditional Cox Proportional Hazards Model id: A numeric identification assigned to each separate C++ class (Note that the id's do not increment from the first to the last data row) start: A time infinitesimally greater than the time of the modification that created this observation (practically, modification time). When a class is introduced to a system, a new observation is entered with start=0 end: Either the time of the next modification, or the end of the observation period, or the time of deletion, whichever comes first. event: event is set to 1 if a defect fix takes place at the time represented by 'end', or 0 otherwise. A class deletion is handled easily by entering a final observation whose event is set to 1 if the class is deleted for corrective maintenance, or 0 otherwise. size: It is a time-dependent covariate and its column carries the number of source Lines of Code of the C++ classes at time 'start'. Blank and comment lines are not counted. state: Initially set to 0, and it becomes 1 after the class experiences an event, and remains at 1 thereafter.

6 features

state (target)nominal2 unique values
0 missing
idnumeric4089 unique values
0 missing
startnumeric8525 unique values
0 missing
endnumeric10599 unique values
0 missing
eventnumeric2 unique values
0 missing
sizenumeric2000 unique values
0 missing
Show all 6 features

107 properties

NumberOfInstances
15545
Number of instances (rows) of the dataset.
NumberOfFeatures
6
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
5
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
AutoCorrelation
0.71
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
0.95
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
0.06
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
0.87
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
0.95
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
0.06
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
0.87
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
0.95
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
0.06
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
0.87
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
0.91
Entropy of the target attribute values.
DecisionStumpAUC
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.07
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.84
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0
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
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.06
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.87
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.06
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.87
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.06
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.87
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
67.14
Percentage of instances belonging to the most frequent class.
MajorityClassSize
10437
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
53.59
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
676568.2
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
5.89
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
585391.15
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
9.98
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
221598.56
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
1.47
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
220055.54
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.93
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
0.57
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
-0.27
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.5
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
32.86
Percentage of instances belonging to the least frequent class.
MinorityClassSize
5108
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.31
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.4
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
1
Number of binary attributes.
PercentageOfBinaryFeatures
16.67
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
83.33
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
16.67
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-1.56
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
217.29
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
-0.18
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
455.87
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
-0.8
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
2097.57
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
0.68
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
1151.82
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
26.9
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
552730.33
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
3.52
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
549107.08
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.06
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.06
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.06
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.07
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.84
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.07
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.84
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.07
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.84
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
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.12
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.72
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

11 tasks

Supervised Classification on mozilla4
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: state
Supervised Classification on mozilla4
0 runs - estimation_procedure: Test on Training Data - target_feature: state
Supervised Classification on mozilla4
0 runs - estimation_procedure: 33% Holdout set - target_feature: state
Supervised Classification on mozilla4
0 runs - estimation_procedure: 10% Holdout set - target_feature: state
Supervised Classification on mozilla4
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: state
Supervised Classification on mozilla4
0 runs - estimation_procedure: Leave one out - target_feature: state
Supervised Classification on mozilla4
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: state
Supervised Classification on mozilla4
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: state
Learning Curve on mozilla4
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: state
Learning Curve on mozilla4
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - target_feature: state
Supervised Data Stream Classification on mozilla4
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: state
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