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mozilla4

mozilla4

active ARFF Publicly available Visibility: public Uploaded 06-10-2014 by Joaquin Vanschoren
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  • mythbusting_1 OpenML100 study_1 study_123 study_135 study_14 study_15 study_20 study_34 study_41 study_7 time_series
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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

107 properties

15545
Number of instances (rows) of the dataset.
6
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.
5
Number of numeric attributes.
1
Number of nominal attributes.
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.
0.06
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
0.84
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.95
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.
-0.18
First quartile of skewness among attributes of the numeric type.
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
0
Standard deviation of the number of distinct values among attributes of the nominal type.
0.06
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
1.47
Mean skewness among attributes of the numeric type.
455.87
First quartile of standard deviation of attributes of the numeric type.
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
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
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.87
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
220055.54
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.06
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
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
0.12
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
67.14
Percentage of instances belonging to the most frequent class.
Minimal entropy among attributes.
-0.8
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.91
Entropy of the target attribute values.
0.72
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
10437
Number of instances belonging to the most frequent class.
-1.93
Minimum kurtosis among attributes of the numeric type.
2097.57
Second quartile (Median) of means among attributes of the numeric type.
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
0.57
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.06
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.07
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
53.59
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0.68
Second quartile (Median) of skewness among attributes of the numeric type.
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.84
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
676568.2
Maximum of means among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
16.67
Percentage of binary attributes.
1151.82
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.92
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.
Maximum mutual information between the nominal attributes and the target attribute.
-0.27
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.07
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.
2
The maximum number of distinct values among attributes of the nominal type.
0.5
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
26.9
Third quartile of kurtosis among attributes of the numeric type.
0.71
Average class difference between consecutive instances.
0.84
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
5.89
Maximum skewness among attributes of the numeric type.
32.86
Percentage of instances belonging to the least frequent class.
83.33
Percentage of numeric attributes.
552730.33
Third quartile of means among attributes of the numeric type.
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
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.06
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
585391.15
Maximum standard deviation of attributes of the numeric type.
5108
Number of instances belonging to the least frequent class.
16.67
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
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
0.07
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.87
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
3.52
Third quartile of skewness among attributes of the numeric type.
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
0.84
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
9.98
Mean kurtosis among attributes of the numeric type.
0.31
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-1.56
First quartile of kurtosis among attributes of the numeric type.
549107.08
Third quartile of standard deviation of attributes of the numeric type.
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
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.06
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
221598.56
Mean of means among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
0.4
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
217.29
First quartile of means among attributes of the numeric type.
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
0.07
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.87
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

17 tasks

103712 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: state
192 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: state
0 runs - estimation_procedure: 33% Holdout set - target_feature: state
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: state
84 runs - estimation_procedure: 10-fold Learning Curve - target_feature: state
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: state
0 runs - target_feature: state
1310 runs - target_feature: state
1306 runs - target_feature: state
0 runs - target_feature: state
0 runs - target_feature: state
0 runs - target_feature: state
0 runs - target_feature: state
0 runs - target_feature: state
0 runs - target_feature: state
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