DeepAI AI Chat
Log In Sign Up

Locally Weighted Regression with different Kernel Smoothers for Software Effort Estimation

09/12/2022
by   Yousef Alqasrawi, et al.
0

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.

READ FULL TEXT
12/16/2020

Testing the Stationarity Assumption in Software Effort Estimation Datasets

Software effort estimation (SEE) models are typically developed based on...
07/04/2021

Analyzing the Stationarity Process in Software Effort Estimation Datasets

Software effort estimation models are typically developed based on an un...
01/25/2021

Violent Crime in London: An Investigation using Geographically Weighted Regression

Violent crime in London is an area of increasing interest following poli...
04/16/2022

FKreg: A MATLAB toolbox for fast Multivariate Kernel Regression

Kernel smooth is the most fundamental technique for data density and reg...
02/11/2021

Empirical Analysis on Productivity Prediction and Locality for Use Case Points Method

Use Case Points (UCP) method has been around for over two decades. Altho...
02/27/2019

Local Bandwidth Estimation via Mixture of Gaussian Processes

Real world data often exhibit inhomogeneity - complexity of the target f...
03/03/2011

Sparse Volterra and Polynomial Regression Models: Recoverability and Estimation

Volterra and polynomial regression models play a major role in nonlinear...