SEffEst: Effort estimation in software projects using fuzzy logic and neural networks
Academia and practitioners confirm that software project effort prediction is crucial for an accurate software project management. However, software development effort estimation is uncertain by nature. Literature has developed methods to improve estimation correctness, using artificial intelligence...
Main Authors: | , , , |
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Format: | Article |
Language: | English |
Published: |
Springer
2012-08-01
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Series: | International Journal of Computational Intelligence Systems |
Subjects: | |
Online Access: | https://www.atlantis-press.com/article/25868001.pdf |
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author | Israel González-Carrasco Ricardo Colomo-Palacios José Luis López-Cuadrado Francisco José García Peñalvo |
author_facet | Israel González-Carrasco Ricardo Colomo-Palacios José Luis López-Cuadrado Francisco José García Peñalvo |
author_sort | Israel González-Carrasco |
collection | DOAJ |
description | Academia and practitioners confirm that software project effort prediction is crucial for an accurate software project management. However, software development effort estimation is uncertain by nature. Literature has developed methods to improve estimation correctness, using artificial intelligence techniques in many cases. Following this path, this paper presents SEffEst, a framework based on fuzzy logic and neural networks designed to increase effort estimation accuracy on software development projects. Trained using ISBSG data, SEffEst presents remarkable results in terms of prediction accuracy. |
first_indexed | 2024-12-12T11:22:51Z |
format | Article |
id | doaj.art-45d8e33a2e144b32b8bfccaee547f68b |
institution | Directory Open Access Journal |
issn | 1875-6883 |
language | English |
last_indexed | 2024-12-12T11:22:51Z |
publishDate | 2012-08-01 |
publisher | Springer |
record_format | Article |
series | International Journal of Computational Intelligence Systems |
spelling | doaj.art-45d8e33a2e144b32b8bfccaee547f68b2022-12-22T00:25:59ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832012-08-015410.1080/18756891.2012.718118SEffEst: Effort estimation in software projects using fuzzy logic and neural networksIsrael González-CarrascoRicardo Colomo-PalaciosJosé Luis López-CuadradoFrancisco José García PeñalvoAcademia and practitioners confirm that software project effort prediction is crucial for an accurate software project management. However, software development effort estimation is uncertain by nature. Literature has developed methods to improve estimation correctness, using artificial intelligence techniques in many cases. Following this path, this paper presents SEffEst, a framework based on fuzzy logic and neural networks designed to increase effort estimation accuracy on software development projects. Trained using ISBSG data, SEffEst presents remarkable results in terms of prediction accuracy.https://www.atlantis-press.com/article/25868001.pdfFuzzy LogicNeural NetworksSoftware EngineeringEffort Estimation |
spellingShingle | Israel González-Carrasco Ricardo Colomo-Palacios José Luis López-Cuadrado Francisco José García Peñalvo SEffEst: Effort estimation in software projects using fuzzy logic and neural networks International Journal of Computational Intelligence Systems Fuzzy Logic Neural Networks Software Engineering Effort Estimation |
title | SEffEst: Effort estimation in software projects using fuzzy logic and neural networks |
title_full | SEffEst: Effort estimation in software projects using fuzzy logic and neural networks |
title_fullStr | SEffEst: Effort estimation in software projects using fuzzy logic and neural networks |
title_full_unstemmed | SEffEst: Effort estimation in software projects using fuzzy logic and neural networks |
title_short | SEffEst: Effort estimation in software projects using fuzzy logic and neural networks |
title_sort | seffest effort estimation in software projects using fuzzy logic and neural networks |
topic | Fuzzy Logic Neural Networks Software Engineering Effort Estimation |
url | https://www.atlantis-press.com/article/25868001.pdf |
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