AdamOptimizer for the optimisation of Use Case Points estimation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F20%3A63526963" target="_blank" >RIV/70883521:28140/20:63526963 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-030-63322-6_63" target="_blank" >http://dx.doi.org/10.1007/978-3-030-63322-6_63</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-63322-6_63" target="_blank" >10.1007/978-3-030-63322-6_63</a>
Alternative languages
Result language
angličtina
Original language name
AdamOptimizer for the optimisation of Use Case Points estimation
Original language description
Use Case Points is considered to be one of the most popular methods to estimate the size of a developed software project. Many approaches have been proposed to optimise Use Case Points. The Algorithmic Optimisation Method uses the Multiple Least Squares method to improve the accuracy of Use Case Points by finding optimal coefficient regressions, based on the historical data. This paper aims to propose a new approach to optimise the Use Case Points method based on Gradient Descent with the support of the TensorFlow package. The significance of its purpose is to conduct a new approach that might lead to more accurate prediction than that of the Use Case Points and the Algorithmic Optimisation Method. As a result, this new approach outweighs both the Use Case Points and the Algorithmic Optimisation Methods. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.
Czech name
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Czech description
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Classification
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)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Advances in Intelligent Systems and Computing Volume 1294
ISBN
978-303063321-9
ISSN
21945357
e-ISSN
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Number of pages
10
Pages from-to
747-756
Publisher name
Springer Science and Business Media Deutschland GmbH
Place of publication
Berlín
Event location
Vsetín
Event date
Oct 14, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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