AI-based models for software effort estimation

Ekrem Kocaguneli*, Ayse Tosun, Ayse Bener

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

19 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings - 36th EUROMICRO Conference on Software Engineering and Advanced Applications, SEAA 2010
Pages323-326
Number of pages4
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event36th EUROMICRO Conference on Software Engineering and Advanced Applications, SEAA 2010 - Lille, France
Duration: 1 Sept 20103 Sept 2010

Publication series

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

Conference

Conference36th EUROMICRO Conference on Software Engineering and Advanced Applications, SEAA 2010
Country/TerritoryFrance
CityLille
Period1/09/103/09/10

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