Evaluating Artificial Neural Networks and Traditional Approaches for Risk Analysis in Software Project Management - A Case Study with PERIL Dataset

Carlos Timoteo, Meuser Valença, Sérgio Fernandes

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.

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Paper Citation


in Harvard Style

Timoteo C., Valença M. and Fernandes S. (2014). Evaluating Artificial Neural Networks and Traditional Approaches for Risk Analysis in Software Project Management - A Case Study with PERIL Dataset . In Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-027-7, pages 472-479. DOI: 10.5220/0004885704720479


in Bibtex Style

@conference{iceis14,
author={Carlos Timoteo and Meuser Valença and Sérgio Fernandes},
title={Evaluating Artificial Neural Networks and Traditional Approaches for Risk Analysis in Software Project Management - A Case Study with PERIL Dataset},
booktitle={Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2014},
pages={472-479},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004885704720479},
isbn={978-989-758-027-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Evaluating Artificial Neural Networks and Traditional Approaches for Risk Analysis in Software Project Management - A Case Study with PERIL Dataset
SN - 978-989-758-027-7
AU - Timoteo C.
AU - Valença M.
AU - Fernandes S.
PY - 2014
SP - 472
EP - 479
DO - 10.5220/0004885704720479