Use Case Points Benchmark Dataset

Published: 1 Mar 2017 | Version 1 | DOI: 10.17632/2rfkjhx3cn.1

Description of this data

This dataset was gathered by us from three software houses. This is real-life dataset. Use Case points method as originated by Karner was used for counting a steps or number of actors.
Attributes are used as follows:
Project_No - only project ID for identification purposes
Simple Actors - Number of actor classify according UCP - simple actors.
Average Actors - Number of actor classify according UCP - average actors.
Complex Actors - Number of actor classify according UCP - complex actors.
UAW - Unadjusted Actor weight, computed by using UCP equation.
Simple UC - Number of use cases classified as simple - UCP number of steps is used.
Average UC - Number of use cases classified as average - UCP number of steps is used.
Complex UC - Number of use cases classified as complex - UCP number of steps is used.
UUCW - Unadjusted UseCase Weight - computed by using UCP equation.
TCF - Technical Complexity Factor
ECF - Enviromental Complexity Factors
Real_P20 - Real_P20 - Real Effort in Person hours, decided by productivity factor (PF = 20).
Real_Effort_Person_Hours - Real Effort (development time) in person-hours.
Sector - Problem domain of project
Language - Programming language used for project.
Methodology - Development methodology used for project development.
ApplicationType - Classification of project type - provided by donator.
DataDonator - anonymized acronym for data donator.

Experiment data files

Related links

This data is associated with the following publication:

Analysis and selection of a regression model for the Use Case Points method using a stepwise approach

Published in: Journal of Systems and Software

Latest version

  • Version 1


    Published: 2017-03-01

    DOI: 10.17632/2rfkjhx3cn.1

    Cite this dataset

    Silhavy, Radek (2017), “Use Case Points Benchmark Dataset”, Mendeley Data, v1


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Downloads: 26


Univerzita Tomase Bati ve Zline


Computer Science, Management, Software Engineering, Applied Computing, Software Engineering Method, Software Development, Empirical Study of Software Engineering, Software Development Effort Estimation, Computer Software, Linear Regression, Linear Regression Model


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