AI-based models for software effort estimation

Ekrem Kocaguneli*, Ayse Tosun, Ayse Bener

*Bu çalışma için yazışmadan sorumlu yazar

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19 Atıf (Scopus)

Özet

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.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığıProceedings - 36th EUROMICRO Conference on Software Engineering and Advanced Applications, SEAA 2010
Sayfalar323-326
Sayfa sayısı4
DOI'lar
Yayın durumuYayınlandı - 2010
Harici olarak yayınlandıEvet
Etkinlik36th EUROMICRO Conference on Software Engineering and Advanced Applications, SEAA 2010 - Lille, France
Süre: 1 Eyl 20103 Eyl 2010

Yayın serisi

AdıProceedings - 36th EUROMICRO Conference on Software Engineering and Advanced Applications, SEAA 2010

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???event.eventtypes.event.conference???36th EUROMICRO Conference on Software Engineering and Advanced Applications, SEAA 2010
Ülke/BölgeFrance
ŞehirLille
Periyot1/09/103/09/10

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