Authors:
Carlos Timoteo
;
Meuser Valença
and
Sérgio Fernandes
Affiliation:
University of Pernambuco, Brazil
Keyword(s):
Software Project, Risk Management, Risk Analysis, Support Vector Machine, MultiLayer Perceptron, Monte Carlo Simulation, Linear Regression Model.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Network Software and Applications
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
Many software project management end in failure. Risk analysis is an essential process to support project success. There is a growing need for systematic methods to supplement expert judgment in order to increase the accuracy in the prediction of risk likelihood and impact. In this paper, we evaluated support vector machine (SVM), multilayer perceptron (MLP), a linear regression model and monte carlo simulation to perform risk analysis based on PERIL data. We have conducted a statistical experiment to determine which is a more accurate method in risk impact estimation. Our experimental results showed that artificial neural network methods proposed in this study outperformed both linear regression and monte carlo simulation.