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