Data
usp05-ft

usp05-ft

active ARFF Publicly available Visibility: public Uploaded 06-10-2014 by Joaquin Vanschoren
0 likes downloaded by 5 people , 5 total downloads 0 issues 0 downvotes
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
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 Jingzhou Li (jingli@ucalgary.ca) 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: USP05-FT: Software effort estimation at feature level 2. Source Information -- Donor: Jingzhou Li (jingli@ucalgary.ca), Guenther Ruhe (ruhe@ucalgary.ca) computer science department University of Calgary, Canada (403) 210-5440 -- Date: December 2005 3. Past Usage: [1]. J.Z. Li, G. Ruhe, A. Al-Emran, M. M. Ritcher, "A Flexible Method for Effort Estimation by Analogy", Empirical Software Engineering, Vol. 12, No. 1, 2007, pp 65-106. [2]. J.Z. Li, G. Ruhe, "A Comparative Study of Attribute Weighting Heuristics for Effort Estimation by Analogy", Proceedings of the ACM-IEEE International Symposium on Empirical Software Engineering (ISESE'06), September 2006, Brazil. 4. Relevant Information: -- This data set was part of USP05 that was collected from university student projects about Web and client/server applications -- The detailed description of the whole data set can be found in reference [1]. 5. Number of Instances: 76 (features) 6. Number of Attributes: 15 (including ID, Effort is the actual effort) 7. Attribute Information: 1. ID: Three digit Object ID, 2. Effort: Actual effort in hours expended on tasks related to implementing the object by all participating persons. 3. IntComplx: Complexity of Internal Calculation (1-VeryLow, 2-Low, 3-Medium, 4-High, 5-VeryHigh ) 4. DataFile: Number of Data Files/Database Tables Accessed (Positive integer) 5. DataEn: Number of Data Entry Items (Positive integer) 6. DataOut: Number of Data Output Items (Positive integer) 7. UFP: Unadjusted Function Point Count (Positive integer) 8. Lang: Language Used (C++, Java, VB, Java Script, VB Script, SQL, Php, Perl, Asp, Html, XML, Others) 9. Tools: Development Tools and Platforms (VJ++, VB, Delphi, VisualCafe, JUnit, PowerBuilder, BorlandC++, Others) 10. ToolExpr: Language and Tool Experience Level (Range of number of months of experience, e.g. [2, 5] for 2 to 5 months, as the minimum experience level is 2 and 5 the maximum in the team) 11. AppExpr: Applications Experience Level (1-VeryLow, 2-Low, 3-Medium, 4-High, 5-VeryHigh) 12. TeamSize: Team size for implementing the object (Range: [a, b], min-max number of persons, e.g. [2, 5]) 13. DBMS: Database Systems (Oracle, Access, SQLServer, MySQL, Others) 14. Method: Methodology (OO, SA, SD, RAD, JAD, MVC, Others) 15. AppType: Type of System/Application Architecture (B/S, C/S, BC/S, Centered, Other) 8. Missing Attribute Values: 37 9. Data

15 features

AppType (target)nominal6 unique values
4 missing
IDnumeric76 unique values
0 missing
Effortnumeric18 unique values
0 missing
IntComplxnumeric5 unique values
0 missing
DataFilenumeric11 unique values
0 missing
DataEnnumeric19 unique values
0 missing
DataOutnumeric8 unique values
2 missing
UFPnumeric24 unique values
2 missing
Langnominal14 unique values
2 missing
Toolsnominal16 unique values
2 missing
ToolExprnominal15 unique values
2 missing
AppExprnumeric5 unique values
0 missing
TeamSizenominal11 unique values
0 missing
DBMSnominal4 unique values
9 missing
Methodnominal7 unique values
14 missing

107 properties

76
Number of instances (rows) of the dataset.
15
Number of attributes (columns) of the dataset.
7
Number of distinct values of the target attribute (if it is nominal).
37
Number of missing values in the dataset.
18
Number of instances with at least one value missing.
8
Number of numeric attributes.
7
Number of nominal attributes.
53.33
Percentage of numeric attributes.
16.1
Third quartile of means among attributes of the numeric type.
0.55
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
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.21
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
215.85
Maximum standard deviation of attributes of the numeric type.
1.32
Percentage of instances belonging to the least frequent class.
46.67
Percentage of nominal attributes.
0.97
Third quartile of mutual information between the nominal attributes and the target attribute.
0.21
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
0.25
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.18
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
2.34
Average entropy of the attributes.
1
Number of instances belonging to the least frequent class.
1.87
First quartile of entropy among attributes.
4.51
Third quartile of skewness among attributes of the numeric type.
0.18
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
0.29
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.55
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
13.26
Mean kurtosis among attributes of the numeric type.
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.26
First quartile of kurtosis among attributes of the numeric type.
42.89
Third quartile of standard deviation of attributes of the numeric type.
0.55
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
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.21
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
48.65
Mean of means among attributes of the numeric type.
0.18
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
2.13
First quartile of means among attributes of the numeric type.
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.21
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
0.25
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.18
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.76
Average mutual information between the nominal attributes and the target attribute.
0.45
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.55
First quartile of mutual information between the nominal attributes and the target attribute.
0.18
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.18
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
0.29
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.55
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
2.1
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0
Number of binary attributes.
1.19
First quartile of skewness among attributes of the numeric type.
0.36
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.55
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
4.79
Standard deviation of the number of distinct values among attributes of the nominal type.
0.21
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
10.43
Average number of distinct values among the attributes of the nominal type.
1.88
First quartile of standard deviation of attributes of the numeric type.
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.21
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
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.18
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
2.86
Mean skewness among attributes of the numeric type.
2.51
Second quartile (Median) of entropy among attributes.
0.18
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.18
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
0.08
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
72.37
Percentage of instances belonging to the most frequent class.
38.94
Mean standard deviation of attributes of the numeric type.
5.43
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.36
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1.19
Entropy of the target attribute values.
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
55
Number of instances belonging to the most frequent class.
1.61
Minimal entropy among attributes.
4.45
Second quartile (Median) of means among attributes of the numeric type.
0.86
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
2.75
Maximum entropy among attributes.
-1.25
Minimum kurtosis among attributes of the numeric type.
0.82
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.18
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.1
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
49.7
Maximum kurtosis among attributes of the numeric type.
1.95
Minimum of means among attributes of the numeric type.
2.27
Second quartile (Median) of skewness among attributes of the numeric type.
0.36
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.75
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
344.28
Maximum of means among attributes of the numeric type.
0.31
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of binary attributes.
7.48
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.2
Number of attributes divided by the number of instances.
0.98
Maximum mutual information between the nominal attributes and the target attribute.
4
The minimal number of distinct values among attributes of the nominal type.
23.68
Percentage of instances having missing values.
2.73
Third quartile of entropy among attributes.
0.25
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
1.57
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
16
The maximum number of distinct values among attributes of the nominal type.
0.45
Minimum skewness among attributes of the numeric type.
3.25
Percentage of missing values.
23.59
Third quartile of kurtosis among attributes of the numeric type.
0.79
Average class difference between consecutive instances.
0.29
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.55
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
6.76
Maximum skewness among attributes of the numeric type.
1.25
Minimum standard deviation of attributes of the numeric type.

13 tasks

321 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: AppType
164 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: AppType
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
Define a new task