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 :
بازگشت