Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/28324
Title: Exploiting the essential assumptions of analogy-based effort estimation
Authors: Kocaguneli, E
Menzies, T
Bener, AB
Keung, JW
Keywords: analogy
k-NN
Software cost estimation
Issue Date: 2012
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on software engineering, 2012, v. 38, no. 2, 5728833, p. 425-438 How to cite?
Journal: IEEE transactions on software engineering 
Abstract: Background: There are too many design options for software effort estimators. How can we best explore them all? Aim: We seek aspects on general principles of effort estimation that can guide the design of effort estimators. Method: We identified the essential assumption of analogy-based effort estimation, i.e., the immediate neighbors of a project offer stable conclusions about that project. We test that assumption by generating a binary tree of clusters of effort data and comparing the variance of supertrees versus smaller subtrees. Results: For 10 data sets (from Coc81, Nasa93, Desharnais, Albrecht, ISBSG, and data from Turkish companies), we found: 1) The estimation variance of cluster subtrees is usually larger than that of cluster supertrees; 2) if analogy is restricted to the cluster trees with lower variance, then effort estimates have a significantly lower error (measured using MRE, AR, and Pred(25) with a Wilcoxon test, 95 percent confidence, compared to nearest neighbor methods that use neighborhoods of a fixed size). Conclusion: Estimation by analogy can be significantly improved by a dynamic selection of nearest neighbors, using only the project data from regions with small variance.
URI: http://hdl.handle.net/10397/28324
ISSN: 0098-5589
EISSN: 1939-3520
DOI: 10.1109/TSE.2011.27
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