A Real Time Extreme Learning Machine for
Software Development Effort Estimation
Kanakasabhapathi
Pillai1 and Muthayyan Jeyakumar2
1Department of Electrical and
Electronics Engineering, Kalaivanar Nagercoil Sudalaimuthu Krishnan College of
Engineering, India
2Department of Computer Applications,
Noorul Islam University, India
Abstract: Software development effort estimation always remains
a challenging task for project managers in a software industry. New techniques
are applied to estimate effort. Evaluation of accuracy is a major activity as
many methods are proposed in the literature. Here, we have developed a new
algorithm called Real Time Extreme Learning Machine (RT-ELM) based on online
sequential learning algorithm. The online sequential learning algorithm is
modified so that the extreme learning machine learns continuously as new
projects are developed in a software development organization. Performance of
the real time extreme learning machine is compared with training and testing
methodology. Studies were also conducted using radial basis function and
additive hidden node. The accuracy of the Real time Extreme Learning machine
with continuous learning is better than the conventional training and testing
method. The results also indicate that the performance of radial basis function
and additive hidden nodes is data dependent. The results are validated using
data from academic setting and industry.
Keywords: Software effort estimation, extreme
learning machine, real time, radial basis function.