Description

Software estimation is a tedious and daunting task in project management and software

development. Software estimators are notorious in predicting software effort and they have been

struggling in the past decades to provide new models to enhance software estimation. The  most

critical and crucial part of software estimation is when estimation is required in  the early stages

of the software life  cycle where the problem to be  solved has not yet been completely revealed.

This  paper presents a novel log-linear regression model based on  the use case point model

(UCP) to calculate the software effort based on  use case diagrams. A fuzzy logic approach is

used to calibrate the productivity factor in the regression model. Moreover, a multilayer

perceptron (MLP) neural network model was developed to predict software effort based on  the

software size and team productivity. Experiments show that the proposed approach out- performs

the original UCP model. Furthermore, a comparison between the MLP and log-linear regression

models was conducted based on  the size of the projects. Results demonstrate that the MLP

model can surpass the regression model when small projects are used, but the log-linear

regression model gives better results when estimating larger projects.