Software cost estimation using neural networks

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Software cost estimation using neural networks

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Software cost estimation using neural networks

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

Lecture Notes in Networks and Systems, Volume 722 LNNS

ISBN

978-3-031-35310-9

ISSN

—

e-ISSN

—

Počet stran výsledku

17

Strana od-do

831-847

Název nakladatele

Springer Science and Business Media Deutschland GmbH

Místo vydání

Berlín

Místo konání akce

Virtual, Online

Datum konání akce

3. 4. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—