DocumentCode
2746151
Title
The effects of data mining techniques on software cost estimation
Author
Lum, Karen T. ; Baker, Daniel R. ; Hihn, Jairus M.
Author_Institution
Jet Propulsion Lab., California Inst. of Technol., Pasadena, CA
fYear
2008
fDate
28-30 June 2008
Firstpage
1
Lastpage
5
Abstract
Current research at JPL incorporates data mining and machine learning techniques to see whether a better software cost model can be developed. 2CEE is a tool developed for developing new software cost estimation models using data mining techniques. The accuracy of these models has been validated internally through leave-one out cross validation. However, the newly generated models have not been validated to see how well they predict in the real world. Our study seeks to find out how well these machine learning based models perform against standard models for eighteen new flight and ground software projects. The accurate performance of the models against current real world projects is extremely important for practitioners to adapt new techniques.
Keywords
data mining; learning (artificial intelligence); software cost estimation; data mining techniques; machine learning techniques; software cost estimation; software cost model; Calibration; Costs; Data mining; Laboratories; Machine learning; Nearest neighbor searches; Predictive models; Propulsion; Software performance; Software tools; Cost estimation; data mining; model performance; modeling software costs;
fLanguage
English
Publisher
ieee
Conference_Titel
Engineering Management Conference, 2008. IEMC Europe 2008. IEEE International
Conference_Location
Estoril
Print_ISBN
978-1-4244-2288-3
Electronic_ISBN
978-1-4244-2289-0
Type
conf
DOI
10.1109/IEMCE.2008.4617949
Filename
4617949
Link To Document