Open AccessAnalysis of task effort estimation accuracy based on use case point size
Author(s) -
Popovic Jovan,
Bojic Dragan,
Korolija Nenad
Publication year - 2015
Publication title -
iet software
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.305
H-Index - 43
eISSN - 1751-8814
pISSN - 1751-8806
DOI - 10.1049/iet-sen.2014.0254
Subject(s) - task (project management) , use case points , estimation , computer science , point (geometry) , set (abstract data type) , point estimation , function point , software , machine learning , project management , data mining , software development , statistics , engineering , systems engineering , software development process , mathematics , geometry , programming language
The use case point (UCP) method is one of the most commonly used size estimation methods in software development. Applicability of UCP size for the project effort estimation is thoroughly investigated; however, little attention is devoted to the effort estimation of particular task types. The authors have created and cross‐compared prediction models for estimating task‐type efforts by means of UCP size using an Online analytical processing model and R packages on a set of 32 real‐world projects, with the goal of facilitating analysis of the correlation between project sizes and effort required to complete task types. Requirements, scoping, functional specification, and functional testing task types have up to two times better estimation accuracies than project effort. Implementation has slightly better accuracy than the project effort, while the other task types are not correlated to the UCP size. Using estimates of the most correlated task types and other techniques, such as expert judgment for others, we improved the overall project effort prediction accuracy and decreased the error from 26 to 16%.