Kernel methods for software effort estimation : Effects of different kernel functions and bandwidths on estimation accuracy

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

33 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)1-24
Journal / PublicationEmpirical Software Engineering
Volume18
Issue number1
Publication statusPublished - Feb 2013
Externally publishedYes

Abstract

Analogy based estimation (ABE) generates an effort estimate for a new software project through adaptation of similar past projects (a.k.a. analogies). Majority of ABE methods follow uniform weighting in adaptation procedure. In this research we investigated non-uniform weighting through kernel density estimation. After an extensive experimentation of 19 datasets, 3 evaluation criteria, 5 kernels, 5 bandwidth values and a total of 2090 ABE variants, we found that: (1) non-uniform weighting through kernel methods cannot outperform uniform weighting ABE and (2) kernel type and bandwidth parameters do not produce a definite effect on estimation performance. In summary simple ABE approaches are able to perform better than much more complex approaches. Hence, - provided that similar experimental settings are adopted - we discourage the use of kernel methods as a weighting strategy in ABE. © 2011 Springer Science+Business Media, LLC.

Research Area(s)

  • Bandwidth, Data mining, Effort estimation, Kernel function