TY - GEN
T1 - AI-based models for software effort estimation
AU - Kocaguneli, Ekrem
AU - Tosun, Ayse
AU - Bener, Ayse
PY - 2010
Y1 - 2010
N2 - Decision making under uncertainty is a critical problem in the field of software engineering. Predicting the software quality or the cost/ effort requires high level expertise. AI based predictor models, on the other hand, are useful decision making tools that learn from past projects' data. In this study, we have built an effort estimation model for a multinational bank to predict the effort prior to projects' development lifecycle. We have collected process, product and resource metrics from past projects together with the effort values distributed among software life cycle phases, i.e. analysis & test, design & development. We have used Clustering approach to form consistent project groups and Support Vector Regression (SVR) to predict the effort. Our results validate the benefits of using AI methods in real life problems. We attain Pred(25) values as high as 78% in predicting future projects.
AB - Decision making under uncertainty is a critical problem in the field of software engineering. Predicting the software quality or the cost/ effort requires high level expertise. AI based predictor models, on the other hand, are useful decision making tools that learn from past projects' data. In this study, we have built an effort estimation model for a multinational bank to predict the effort prior to projects' development lifecycle. We have collected process, product and resource metrics from past projects together with the effort values distributed among software life cycle phases, i.e. analysis & test, design & development. We have used Clustering approach to form consistent project groups and Support Vector Regression (SVR) to predict the effort. Our results validate the benefits of using AI methods in real life problems. We attain Pred(25) values as high as 78% in predicting future projects.
UR - http://www.scopus.com/inward/record.url?scp=78449278878&partnerID=8YFLogxK
U2 - 10.1109/SEAA.2010.19
DO - 10.1109/SEAA.2010.19
M3 - Conference contribution
AN - SCOPUS:78449278878
SN - 9780769541709
T3 - Proceedings - 36th EUROMICRO Conference on Software Engineering and Advanced Applications, SEAA 2010
SP - 323
EP - 326
BT - Proceedings - 36th EUROMICRO Conference on Software Engineering and Advanced Applications, SEAA 2010
T2 - 36th EUROMICRO Conference on Software Engineering and Advanced Applications, SEAA 2010
Y2 - 1 September 2010 through 3 September 2010
ER -