usp05
OpenML dataset with id 1047
Guenther Ruhe, Jingzhou Li
Full work available at URL: https://api.openml.org/data/v1/download/53930/usp05.arff
Upload date: 6 October 2014
Dataset Characteristics
Number of classes: 11
Number of features: 17 (numeric: 3, symbolic: 14 and in total binary: 0 )
Number of instances: 203
Number of instances with missing values: 0
Number of missing values: 0
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/
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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
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