A very early estimation of software development time and effort using neural networks

In spite of years of research and development, formal structured estimation of time and effort required to develop a Management Information System (MIS) is still an open problem. Usual estimation techniques applied by now are supported by the not so realistic premise of requirements stability, and o...

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Autores principales: Luna, Carlos Daniel, Segovia, Javier, Salvetto, Pedro F., Martínez, Milton F.
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2004
Materias:
Ciencias Informáticas
base de datos
SOFTWARE ENGINEERING
Neural nets
Software
Software development
Time and Effort
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/22320
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-22320
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
base de datos
SOFTWARE ENGINEERING
Neural nets
Software
Software development
Time and Effort
spellingShingle Ciencias Informáticas
base de datos
SOFTWARE ENGINEERING
Neural nets
Software
Software development
Time and Effort
Luna, Carlos Daniel
Segovia, Javier
Salvetto, Pedro F.
Martínez, Milton F.
A very early estimation of software development time and effort using neural networks
topic_facet Ciencias Informáticas
base de datos
SOFTWARE ENGINEERING
Neural nets
Software
Software development
Time and Effort
description In spite of years of research and development, formal structured estimation of time and effort required to develop a Management Information System (MIS) is still an open problem. Usual estimation techniques applied by now are supported by the not so realistic premise of requirements stability, and often human experts are required to apply them. This paper considers models of estimation based on metrics available on early design phase. Our research work aims to develop formal estimation models for time and effort needed for MIS development. These models use development team efficiency, requirements volatility, development speed and system complexity as input parameters. We also identify which input metrics are adequate for measuring system’s cognitive complexity and found that useful metrics can be obtained automatically from the system users´ data views very early on the life cycle with independence of the technology used and without human intervention. We tested the metrics estimation capability using Artificial Neural Networks (ANN), and thus confirmed an existing functional relation among input and output metrics (time and effort). Once trained, the ANN predicts effort needed with a 15% average error and time needed with a 30% average error.
format Objeto de conferencia
Objeto de conferencia
author Luna, Carlos Daniel
Segovia, Javier
Salvetto, Pedro F.
Martínez, Milton F.
author_facet Luna, Carlos Daniel
Segovia, Javier
Salvetto, Pedro F.
Martínez, Milton F.
author_sort Luna, Carlos Daniel
title A very early estimation of software development time and effort using neural networks
title_short A very early estimation of software development time and effort using neural networks
title_full A very early estimation of software development time and effort using neural networks
title_fullStr A very early estimation of software development time and effort using neural networks
title_full_unstemmed A very early estimation of software development time and effort using neural networks
title_sort very early estimation of software development time and effort using neural networks
publishDate 2004
url http://sedici.unlp.edu.ar/handle/10915/22320
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