Home // ICSEA 2012, The Seventh International Conference on Software Engineering Advances // View article

Learning Best K analogies from Data Distribution for Case-Based Software Effort Estimation

Authors:
Mohammad Azzeh
Yousef Elsheikg

Keywords: Software Effort Estimation; Case-Based Reasoning; Adjustment Techniques

Abstract:
Case-Based Reasoning (CBR) has been widely used to generate good software effort estimates. The predictive performance of CBR is a dataset dependent and subject to extremely large space of configuration possibilities. Regardless of the type of adaptation technique, deciding on the optimal number of similar cases to be used before applying CBR is a key challenge. In this paper we propose a new technique based on Bisecting k-medoids clustering algorithm to better understanding the structure of a dataset and discovering the optimal cases for each individual project by excluding irrelevant cases. Results obtained showed that understanding of the data characteristic prior prediction stage can help in automatically finding the best number of cases for each test project. Performance figures of the proposed estimation method are better than those of other regular K-based CBR methods.

Pages: 341 to 347

Copyright: Copyright (c) IARIA, 2012

Publication date: November 18, 2012

Published in: conference

ISSN: 2308-4235

ISBN: 978-1-61208-230-1

Location: Lisbon, Portugal

Dates: from November 18, 2012 to November 23, 2012