Title :
AI-Based Models for Software Effort Estimation
Author :
Kocaguneli, Ekrem ; Tosun, Ayse ; Bener, Ayse
Author_Institution :
Dept. of Comput. Eng., Bogazici Univ., Istanbul, Turkey
Abstract :
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.
Keywords :
decision making; project management; regression analysis; software quality; support vector machines; uncertainty handling; AI based predictor model; Support Vector Regression; clustering approach; decision making; effort estimation model; project development lifecycle; software engineering; software life cycle; software quality; support vector regression; Biological system modeling; Data models; Estimation; Prediction algorithms; Predictive models; Software;
Conference_Titel :
Software Engineering and Advanced Applications (SEAA), 2010 36th EUROMICRO Conference on
Conference_Location :
Lille
Print_ISBN :
978-1-4244-7901-6
DOI :
10.1109/SEAA.2010.19