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qqdefects_numeric

qqdefects_numeric

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  • Chemistry Life Science PROMISE
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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 Norman Fenton 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. Qualitative and quantitative data about 31 projects completed in a consumer electronics company (one row per project). There is a mixture of qualitative attributes (these are measured on a 5 point ranked scale VL, L, M, H, VH) and quantitative attributes whose scale is stated. from.. Title: Project Data Incorporating Qualitative Factors for Improved Software Defect; Author(s): Norman Fenton and Martin Neil and William Marsh and Peter Hearty and Lukasz Radlinski and Paul Krause; Published in: in Proceedings of the PROMISE workshop; Year: 2007; attributes: S1 Relevant Experience of Spec & Doc Staff S2 Quality of Documentation inspected S3 Regularity of Spec & Doc Reviews S4 Standard Procedures Followed S5 Quality of Documentation inspected S6 Spec Defects Discovered in Review S7 Requirements Stability F1 Complexity of new functionality F2 Scale of New functionality implemented F3 Total no. of Inputs and Outputs D1 Relevant Development Staff Experience D2 Programmer capability D3 Defined processes followed D4 Development Staff motivation T1 Testing Process Well Defined T2 Testing Staff Experience T3 Testing Staff Experience T4 Quality of Documented Test Cases P1 Dev. Staff Training Quality P2 Requirements Management P3 Project Planning P4 Scale of Distributed Communication P5 Stake-holder involvement P6 Stake-holder involvement P7 Vendor Management P8 Internal communication/interaction P9 Process Maturity E Total Effort K KLOC L Language TD Testing Defects (Pre+ Post)

31 features

TD (target)numeric31 unique values
0 missing
S1nominal4 unique values
1 missing
S2nominal4 unique values
0 missing
S3nominal3 unique values
1 missing
S4nominal4 unique values
0 missing
S5nominal4 unique values
0 missing
S6nominal4 unique values
2 missing
S7nominal5 unique values
0 missing
F1nominal4 unique values
0 missing
F2nominal5 unique values
0 missing
F3nominal3 unique values
0 missing
D1nominal5 unique values
0 missing
D2nominal5 unique values
0 missing
D3nominal4 unique values
0 missing
D4nominal3 unique values
0 missing
T1nominal3 unique values
0 missing
T2nominal5 unique values
1 missing
T3nominal5 unique values
0 missing
T4nominal3 unique values
0 missing
P1nominal4 unique values
0 missing
P2nominal3 unique values
0 missing
P3nominal5 unique values
0 missing
P4nominal4 unique values
2 missing
P5nominal3 unique values
0 missing
P6nominal3 unique values
0 missing
P7nominal3 unique values
24 missing
P8nominal3 unique values
0 missing
P9nominal3 unique values
0 missing
Enumeric31 unique values
0 missing
Knumeric27 unique values
2 missing
Lnominal2 unique values
0 missing
Show all 31 features

107 properties

NumberOfInstances
31
Number of instances (rows) of the dataset.
NumberOfFeatures
31
Number of attributes (columns) of the dataset.
NumberOfClasses
0
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
33
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
29
Number of instances with at least one value missing.
NumberOfNumericFeatures
3
Number of numeric attributes.
NumberOfSymbolicFeatures
28
Number of nominal attributes.
REPTreeDepth3Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
DecisionStumpKappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
MaxMeansOfNumericAtts
16141.88
Maximum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
1.7
Second quartile (Median) of skewness among attributes of the numeric type.
RandomTreeDepth1AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Dimensionality
1
Number of attributes divided by the number of instances.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
PercentageOfBinaryFeatures
3.23
Percentage of binary attributes.
Quartile2StdDevOfNumericAtts
510.92
Second quartile (Median) of standard deviation of attributes of the numeric type.
RandomTreeDepth1ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
MaxNominalAttDistinctValues
5
The maximum number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
1.4
Minimum skewness among attributes of the numeric type.
PercentageOfInstancesWithMissingValues
93.55
Percentage of instances having missing values.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
RandomTreeDepth1Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
J48.00001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
MaxSkewnessOfNumericAtts
1.74
Maximum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
40.73
Minimum standard deviation of attributes of the numeric type.
PercentageOfMissingValues
3.43
Percentage of missing values.
Quartile3KurtosisOfNumericAtts
2.84
Third quartile of kurtosis among attributes of the numeric type.
AutoCorrelation
-452.83
Average class difference between consecutive instances.
RandomTreeDepth2AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
J48.00001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
MaxStdDevOfNumericAtts
13771.05
Maximum standard deviation of attributes of the numeric type.
MinorityClassPercentage
Percentage of instances belonging to the least frequent class.
PercentageOfNumericFeatures
9.68
Percentage of numeric attributes.
Quartile3MeansOfNumericAtts
16141.88
Third quartile of means among attributes of the numeric type.
CfsSubsetEval_DecisionStumpAUC
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
RandomTreeDepth2ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
J48.00001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
MeanAttributeEntropy
Average entropy of the attributes.
MinorityClassSize
Number of instances belonging to the least frequent class.
PercentageOfSymbolicFeatures
90.32
Percentage of nominal attributes.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
CfsSubsetEval_DecisionStumpErrRate
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
RandomTreeDepth2Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
J48.0001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
MeanKurtosisOfNumericAtts
2.33
Mean kurtosis among attributes of the numeric type.
NaiveBayesAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile3SkewnessOfNumericAtts
1.74
Third quartile of skewness among attributes of the numeric type.
CfsSubsetEval_DecisionStumpKappa
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
RandomTreeDepth3AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
J48.0001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
MeanMeansOfNumericAtts
5542.16
Mean of means among attributes of the numeric type.
NaiveBayesErrRate
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Quartile1KurtosisOfNumericAtts
1.66
First quartile of kurtosis among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
13771.05
Third quartile of standard deviation of attributes of the numeric type.
CfsSubsetEval_NaiveBayesAUC
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
RandomTreeDepth3ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
J48.0001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
NaiveBayesKappa
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Quartile1MeansOfNumericAtts
39.36
First quartile of means among attributes of the numeric type.
REPTreeDepth1AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
CfsSubsetEval_NaiveBayesErrRate
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
RandomTreeDepth3Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
J48.001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
NumberOfBinaryFeatures
1
Number of binary attributes.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
REPTreeDepth1ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
CfsSubsetEval_NaiveBayesKappa
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
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
StdvNominalAttDistinctValues
0.88
Standard deviation of the number of distinct values among attributes of the nominal type.
J48.001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
MeanNominalAttDistinctValues
3.79
Average number of distinct values among the attributes of the nominal type.
Quartile1SkewnessOfNumericAtts
1.4
First quartile of skewness among attributes of the numeric type.
REPTreeDepth1Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
CfsSubsetEval_kNN1NErrRate
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
kNN1NAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
J48.001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MeanSkewnessOfNumericAtts
1.61
Mean skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
40.73
First quartile of standard deviation of attributes of the numeric type.
REPTreeDepth2AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
CfsSubsetEval_kNN1NKappa
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
kNN1NErrRate
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
MajorityClassPercentage
Percentage of instances belonging to the most frequent class.
MeanStdDevOfNumericAtts
4774.23
Mean standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
REPTreeDepth2ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
ClassEntropy
Entropy of the target attribute values.
kNN1NKappa
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
MajorityClassSize
Number of instances belonging to the most frequent class.
MinAttributeEntropy
Minimal entropy among attributes.
Quartile2KurtosisOfNumericAtts
2.49
Second quartile (Median) of kurtosis among attributes of the numeric type.
REPTreeDepth2Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
DecisionStumpAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
MaxAttributeEntropy
Maximum entropy among attributes.
MinKurtosisOfNumericAtts
1.66
Minimum kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
445.23
Second quartile (Median) of means among attributes of the numeric type.
REPTreeDepth3ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
DecisionStumpErrRate
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
MaxKurtosisOfNumericAtts
2.84
Maximum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
39.36
Minimum of means among attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Show all 107 properties

13 tasks

Supervised Regression on qqdefects_numeric
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: TD
Supervised Regression on qqdefects_numeric
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: mean_absolute_error - target_feature: TD
Clustering on qqdefects_numeric
0 runs - estimation_procedure: 50 times Clustering
Clustering on qqdefects_numeric
0 runs - estimation_procedure: 50 times Clustering
Clustering on qqdefects_numeric
0 runs - estimation_procedure: 50 times Clustering
Clustering on qqdefects_numeric
0 runs - estimation_procedure: 50 times Clustering
Clustering on qqdefects_numeric
0 runs - estimation_procedure: 50 times Clustering
Clustering on qqdefects_numeric
0 runs
Clustering on qqdefects_numeric
0 runs - estimation_procedure: 50 times Clustering
Clustering on qqdefects_numeric
0 runs - estimation_procedure: 50 times Clustering
Clustering on qqdefects_numeric
0 runs - estimation_procedure: 50 times Clustering
Clustering on qqdefects_numeric
0 runs - estimation_procedure: 50 times Clustering
Clustering on qqdefects_numeric
0 runs - estimation_procedure: 50 times Clustering
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