Software cost estimation using neural networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F23%3A63572978" target="_blank" >RIV/70883521:28140/23:63572978 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/chapter/10.1007/978-3-031-35311-6_77" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-35311-6_77</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-35311-6_77" target="_blank" >10.1007/978-3-031-35311-6_77</a>

Result language

angličtina

Original language name

Software cost estimation using neural networks

Original language description

Software Cost Estimation (SCE) is one of the most vital parts when starting a new software engineering project; it helps with allocating resources, managing risks, making informed decisions, and stands in correlation with the success or the failure of a project. Because Software Cost Estimation (SCE) is prone to human bias, solutions started being researched with the aid of Artificial Intelligence (AI) and Machine Learning (ML). This paper will investigate the importance of Software Cost Estimation (SCE). Further, the existing taxonomies and methodologies regarding using neural networks with Software Cost estimation will be compared (COCOMO, GEHO-ANN, OLCE, and -ANN-NEAT). This will be done using evaluation metrics such as RMSE, MMRE, PRED, MAE, etc. After, further research is proposed on why using Deep Reinforcement Learning (DRL) could be very beneficial for developing Software Cost Prediction Models. This technique combines Deep Learning (DL) and Machine Learning (ML) and can solve complex tasks with many variables and a rapidly developing environment. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Lecture Notes in Networks and Systems, Volume 722 LNNS

ISBN

978-3-031-35310-9

ISSN

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e-ISSN

—

Number of pages

17

Pages from-to

831-847

Publisher name

Springer Science and Business Media Deutschland GmbH

Place of publication

Berlín

Event location

Virtual, Online

Event date

Apr 3, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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