Review of current data mining techniques used in the software effort estimation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F20%3A63526959" target="_blank" >RIV/70883521:28140/20:63526959 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007%2F978-3-030-63322-6_32" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-030-63322-6_32</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-63322-6_32" target="_blank" >10.1007/978-3-030-63322-6_32</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Review of current data mining techniques used in the software effort estimation

Popis výsledku v původním jazyce

Data Mining is a method of finding patterns from vast quantities of data and information. The data sources include databases, data centres, the internet, and other data storage forms; or data that is dynamically streaming into the network. Estimation of effort is very important in the cost estimation of a software development project, and very critical in the software life development cycle planning process. This paper offers a description of the latest data mining techniques used in estimating software effort, and these techniques are divided into two, namely: Classical and Modern, based on when they were developed and when they started to be used in business administration. The Classical techniques are the ones that have been in use for decades and are still relevant until today, while the Modern ones are the ones that have been introduced recently and have gained wide acceptance in the system. The Classical techniques are Statistical methods, Nearest Neighbours, Clustering and Regression Analysis, while Neural Networks, Rule Induction Systems and Decision Trees are included in the Modern techniques. This paper offers an overview of these strategies in terms of their features, benefits, drawbacks and use areas. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

Název v anglickém jazyce

Review of current data mining techniques used in the software effort estimation

Popis výsledku anglicky

Data Mining is a method of finding patterns from vast quantities of data and information. The data sources include databases, data centres, the internet, and other data storage forms; or data that is dynamically streaming into the network. Estimation of effort is very important in the cost estimation of a software development project, and very critical in the software life development cycle planning process. This paper offers a description of the latest data mining techniques used in estimating software effort, and these techniques are divided into two, namely: Classical and Modern, based on when they were developed and when they started to be used in business administration. The Classical techniques are the ones that have been in use for decades and are still relevant until today, while the Modern ones are the ones that have been introduced recently and have gained wide acceptance in the system. The Classical techniques are Statistical methods, Nearest Neighbours, Clustering and Regression Analysis, while Neural Networks, Rule Induction Systems and Decision Trees are included in the Modern techniques. This paper offers an overview of these strategies in terms of their features, benefits, drawbacks and use areas. © 2020, The Editor(s) (if applicable) and 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

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

Advances in Intelligent Systems and Computing Volume 1294

ISBN

978-303063321-9

ISSN

21945357

e-ISSN

—

Počet stran výsledku

16

Strana od-do

393-408

Název nakladatele

Springer Science and Business Media Deutschland GmbH

Místo vydání

Berlín

Místo konání akce

Vsetín

Datum konání akce

14. 10. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—