An over-sampling method for analogy-based software effort estimation

Authors:
Yasutaka Kamei;Jacky Keung;Akito Monden;Ken-ichi Matsumoto
Affiliations:
Nara Institute of Science and Technology, Ikoma, Japan;National ICT Australia Ltd., Sydney, Australia;Nara Institute of Science and Technology, Ikoma, Japan;Nara Institute of Science and Technology, Ikoma, Japan
Venue:
Proceedings of the Second ACM-IEEE international symposium on Empirical software engineering and measurement
Year:
2008

Citing 6
Cited 0

Estimating Software Project Effort Using Analogies

IEEE Transactions on Software Engineering
A Simulation Study of the Model Evaluation Criterion MMRE

IEEE Transactions on Software Engineering
The Effects of Over and Under Sampling on Fault-prone Module Detection

ESEM '07 Proceedings of the First International Symposium on Empirical Software Engineering and Measurement
Improving analogy-based software cost estimation by a resampling method

Information and Software Technology
Optimising Project Feature Weights for Analogy-Based Software Cost Estimation using the Mantel Correlation

APSEC '07 Proceedings of the 14th Asia-Pacific Software Engineering Conference
SMOTE: synthetic minority over-sampling technique

Journal of Artificial Intelligence Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes a novel method to generate synthetic projectcases and add them to a fit dataset for the purpose of improving the performance of analogy-based software effort estimation. The proposed method extends conventional over-sampling method, which is a preprocessing procedure for n-group classification problems, which makes it suitable for any imbalanced dataset to be used in analogy-based system. We experimentally evaluated the effect of the over-sampling method to improve the performance of the analogy-based software effort estimation by using the Desharnais dataset. Results show significant improvement to the estimation accuracy by using our approach.