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Authors: Efi Papatheocharous and Andreas S. Andreou

Affiliation: University of Cyprus, Cyprus

Keyword(s): Artificial Neural Networks, Genetic Algorithms, Software Cost Estimation.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Databases and Information Systems Integration ; Enterprise Information Systems ; Evolutionary Programming ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Information Systems Analysis and Specification ; 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 ; Software Engineering ; Software Measurement ; Theory and Methods

Abstract: Reliable and accurate software cost estimations have always been a challenge especially for people involved in project resource management. The challenge is amplified due to the high level of complexity and uniqueness of the software process. The majority of estimation methods proposed fail to produce successful cost forecasting and neither resolve to explicit, measurable and concise set of factors affecting productivity. Throughout the software cost estimation literature software size is usually proposed as one of the most important attributes affecting effort and is used to build cost models. This paper aspires to provide size and effort-based estimations for the required software effort of new projects based on data obtained from past completed projects. The modelling approach utilises Artificial Neural Networks (ANN) with a random sliding window input and output method using holdout samples and moreover, a Genetic Algorithm (GA) undertakes to evolve the inputs and internal hidden architectures and to reduce the Mean Relative Error (MRE). The obtained optimal ANN topologies and input and output methods for each dataset are presented, discussed and compared with a classic MLR model. (More)

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Paper citation in several formats:
Papatheocharous, E. and S. Andreou, A. (2008). SIZE AND EFFORT-BASED COMPUTATIONAL MODELS FOR SOFTWARE COST PREDICTION. In Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 4: ICEIS; ISBN 978-989-8111-36-4; ISSN 2184-4992, SciTePress, pages 57-64. DOI: 10.5220/0001708800570064

@conference{iceis08,
author={Efi Papatheocharous. and Andreas {S. Andreou}.},
title={SIZE AND EFFORT-BASED COMPUTATIONAL MODELS FOR SOFTWARE COST PREDICTION},
booktitle={Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 4: ICEIS},
year={2008},
pages={57-64},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001708800570064},
isbn={978-989-8111-36-4},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the Tenth International Conference on Enterprise Information Systems - Volume 4: ICEIS
TI - SIZE AND EFFORT-BASED COMPUTATIONAL MODELS FOR SOFTWARE COST PREDICTION
SN - 978-989-8111-36-4
IS - 2184-4992
AU - Papatheocharous, E.
AU - S. Andreou, A.
PY - 2008
SP - 57
EP - 64
DO - 10.5220/0001708800570064
PB - SciTePress

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