Application of machine learning methods for software effort prediction

Authors:
Ruchika Malhotra;Arvinder Kaur;Yogesh Singh
Affiliations:
University School of Information Technology;University School of Information Technology;Indraprastha University
Venue:
ACM SIGSOFT Software Engineering Notes
Year:
2010

Citing 7
Cited 0

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Abstract

Software effort estimation is an important area in the field of software engineering. If the software development effort is over estimated it may lead to tight time schedules and thus quality and testing of software may be compromised. In contrast, if the software development effort is underestimated it may lead to over allocation of man power and resource. There are many models proposed in the literature for estimating software effort. In this paper, we analyze machine learning methods in order to develop models to predict software development effort we used Maxwell data consisting 63 projects. The results show that linear regression, MSP and M5Rules are effective methods for predicting software development effort.