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usp05

usp05

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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 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: Software effort estimation at project, feature and requirement levels 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 splited into USP05-RQ nad USP05-FT for requirements and feature respectively. USP05-RQ and USP05-T were used for software effort estimation by analogy in the above two references. -- This data set was collected from university student projects -- The detailed description of the whole data set can be found in reference [1]. 5. Number of Instances: 203 (6 projects, 121 requirements, 76 features) 6. Number of Attributes: 17 (including ID and Object Type, Effort is the actual effort) 7. Attribute Information: 1. ID: Three digit Object ID, 2. ObjType: Object type (PJ-project, FT-feature, RQ-requirement) 3. Effort: Actual effort in hours expended on tasks related to implementing the object by all participating persons. 4. Funct%: Percentage of Functionality of features or requirements ({1-Internal process, 2-Data entry/ Modification/ Deletion, 3-Output form(screen), 4-Data query from database/ file, 5-Printing, 6-Report, 7-Other}) 5. IntComplx: Complexity of Internal Calculation (1-VeryLow, 2-Low, 3-Medium, 4-High, 5-VeryHigh ) 6. DataFile: Number of Data Files/Database Tables Accessed (Positive integer) 7. DataEn: Number of Data Entry Items (Positive integer) 8. DataOut: Number of Data Output Items (Positive integer) 9. UFP: Unadjusted Function Point Count (Positive integer) 10. Lang: Language Used (C++, Java, VB, Java Script, VB Script, SQL, Php, Perl, Asp, Html, XML, Others) 11. Tools: Development Tools and Platforms (VJ++, VB, Delphi, VisualCafe, JUnit, PowerBuilder, BorlandC++, Others) 12. 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) 13. AppExpr: Applications Experience Level (1-VeryLow, 2-Low, 3-Medium, 4-High, 5-VeryHigh) 14. TeamSize: Team size for implementing the object (Range: [a, b], min-max number of persons, e.g. [2, 5]) 15. DBMS: Database Systems (Oracle, Access, SQLServer, MySQL, Others) 16. Method: Methodology (OO, SA, SD, RAD, JAD, MVC, Others) 17. AppType: ype of System/Application Architecture (B/S, C/S, BC/S, Centered, Other) 8. Missing Attribute Values: 83 9. Data

17 features

AppType (target)nominal11 unique values
0 missing
IDnumeric203 unique values
0 missing
ObjTypenominal3 unique values
0 missing
Effortnumeric34 unique values
0 missing
FunctPercentnominal112 unique values
0 missing
IntComplxnominal9 unique values
0 missing
DataFilenominal17 unique values
0 missing
DataEnnominal34 unique values
0 missing
DataOutnominal26 unique values
0 missing
UFPnominal68 unique values
0 missing
Langnominal28 unique values
0 missing
Toolsnominal35 unique values
0 missing
ToolExprnominal30 unique values
0 missing
AppExprnumeric6 unique values
0 missing
TeamSizenominal24 unique values
0 missing
DBMSnominal5 unique values
0 missing
Methodnominal11 unique values
0 missing

107 properties

203
Number of instances (rows) of the dataset.
17
Number of attributes (columns) of the dataset.
11
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.
3
Number of numeric attributes.
14
Number of nominal attributes.
1.44
Third quartile of mutual information between the nominal attributes and the target attribute.
0.19
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
0.26
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.68
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
3.37
Average entropy of the attributes.
1
Number of instances belonging to the least frequent class.
82.35
Percentage of nominal attributes.
8.7
Third quartile of skewness among attributes of the numeric type.
0.67
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
0.53
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
29.14
Mean kurtosis among attributes of the numeric type.
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
2.5
First quartile of entropy among attributes.
265.54
Third quartile of standard deviation of attributes of the numeric type.
0.89
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
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.19
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
160.6
Mean of means among attributes of the numeric type.
0.18
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-1.36
First quartile of kurtosis among attributes of the numeric type.
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.19
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
0.26
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.68
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.93
Average mutual information between the nominal attributes and the target attribute.
0.71
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
2.99
First quartile of means among attributes of the numeric type.
0.34
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.67
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
0.53
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
2.64
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.
0.46
First quartile of mutual information between the nominal attributes and the target attribute.
0.33
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.89
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
29.02
Standard deviation of the number of distinct values among attributes of the nominal type.
0.19
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
29.5
Average number of distinct values among the attributes of the nominal type.
-0.07
First quartile of skewness among attributes of the numeric type.
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.19
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
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.68
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
2.97
Mean skewness among attributes of the numeric type.
1.39
First quartile of standard deviation of attributes of the numeric type.
0.34
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.67
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
0.14
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
55.17
Percentage of instances belonging to the most frequent class.
100.35
Mean standard deviation of attributes of the numeric type.
3.15
Second quartile (Median) of entropy among attributes.
0.33
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1.94
Entropy of the target attribute values.
0.77
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
112
Number of instances belonging to the most frequent class.
1.13
Minimal entropy among attributes.
-1.29
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
6.25
Maximum entropy among attributes.
-1.36
Minimum kurtosis among attributes of the numeric type.
11.59
Second quartile (Median) of means among attributes of the numeric type.
0.34
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.32
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
90.09
Maximum kurtosis among attributes of the numeric type.
2.99
Minimum of means among attributes of the numeric type.
0.77
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.33
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.41
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
467.22
Maximum of means among attributes of the numeric type.
0.12
Minimal mutual information between the nominal attributes and the target attribute.
0.29
Second quartile (Median) of skewness among attributes of the numeric type.
34.12
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.08
Number of attributes divided by the number of instances.
1.86
Maximum mutual information between the nominal attributes and the target attribute.
3
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of binary attributes.
3.96
Third quartile of entropy among attributes.
0.26
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
2.09
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
112
The maximum number of distinct values among attributes of the nominal type.
-0.07
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
90.09
Third quartile of kurtosis among attributes of the numeric type.
0.72
Average class difference between consecutive instances.
0.53
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
8.7
Maximum skewness among attributes of the numeric type.
1.39
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
467.22
Third quartile of means among attributes of the numeric type.
0.89
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
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.19
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
265.54
Maximum standard deviation of attributes of the numeric type.
0.49
Percentage of instances belonging to the least frequent class.
17.65
Percentage of numeric attributes.

6 tasks

360 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: AppType
159 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: AppType
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: AppType
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
Define a new task