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Estimating software effort has been a largely unsolved problem for decades.One of the main reasons that hinders building accurate estimation models is theoften heterogeneous nature of software data with a complex structure.Typically, building effort estimation models from local data tends to be moreaccurate than using the entire data. Previous studies have focused on the useof clustering techniques and decision trees to generate local and coherent datathat can help in building local prediction models. However, these approachesmay fall short in some aspect due to limitations in finding optimal clustersand processing noisy data. In this paper we used a more sophisticated localityapproach that can mitigate these shortcomings that is Locally WeightedRegression (LWR). This method provides an efficient solution to learn fromlocal data by building an estimation model that combines multiple localregression models in k-nearest-neighbor based model. The main factor affectingthe accuracy of this method is the choice of the kernel function used to derivethe weights for local regression models. This paper investigates the effects ofchoosing different kernels on the performance of Locally Weighted Regression ofa software effort estimation problem. After comprehensive experiments with 7datasets, 10 kernels, 3 polynomial degrees and 4 bandwidth values with a totalof 840 Locally Weighted Regression variants, we found that: 1) Uniform kernelfunctions cannot outperform non-uniform kernel functions, and 2) kernel type,polynomial degrees and bandwidth parameters have no specific effect on theestimation accuracy.

Locally Weighted Regression with different Kernel Smoothers for Software Effort Estimation