Paper Info
Title
Software development effort estimation in academic environments applying a general regression neural network involving size and people factors
Abstract
In this research a general regression neural network (GRNN) was applied for estimating the development effort in software projects that have been developed in laboratory learning environments. The independent variables of the GRNN were two size measures as well as a developer measure. This GRNN was trained from a dataset of projects developed from the year 2005 to the year 2008 and then this GRNN was validated by estimating the effort of a new dataset integrated by projects developed from the year 2009 o the first months of the year 2010. Accuracy results from the GRNN model were compared with a statistical regression model. Results suggest that a GRNN could be used for estimating the development effort of software projects when two kinds of lines of code as well as the programming language experience of developers are used as independent variables.
Year
DOI
Venue
2011
10.1007/978-3-642-21587-2_29
MCPR
Keywords
Field
DocType
programming language experience,software project,new dataset,development effort,accuracy result,academic environment,developer measure,general regression neural network,statistical regression model,software development effort estimation,independent variable,grnn model,people factor,software engineering,statistical regression
General regression neural network,Regression analysis,Computer science,Software development effort estimation,Software,Variables,Artificial intelligence,Machine learning,Source lines of code
Conference
Citations 
PageRank 
References 
2
0.38
19
Authors
3
Name
Order
Citations
PageRank
Cuauhtémoc López-Martín18611.13
Arturo Chavoya2728.42
M. E. Meda-Campaña321.06