Towards a correction factors-based software productivity using ensemble approach for early software development effort estimation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F22%3A63555918" target="_blank" >RIV/70883521:28140/22:63555918 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-031-09070-7_35" target="_blank" >http://dx.doi.org/10.1007/978-3-031-09070-7_35</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-09070-7_35" target="_blank" >10.1007/978-3-031-09070-7_35</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Towards a correction factors-based software productivity using ensemble approach for early software development effort estimation

Popis výsledku v původním jazyce

Accuracy of effort estimation is one of the necessary conditions for efficiently managing software development projects. Since the information available in the early stages of software development is insufficient, software sizing metrics are considered critical factors for effort estimation. However, there is no consistent method for converting software sizing into the corresponding effort. Previous estimation methods have not considered software productivity a critical factor in estimating effort based on software sizing. This paper proposes a software productivity model based on correction factors in the Optimizing Correction Factors method through an ensemble construction mechanism of three popular machine learning techniques. The results show that using the proposed software productivity minimizes the estimation error of the methods compared to using fixed productivity metrics. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Název v anglickém jazyce

Towards a correction factors-based software productivity using ensemble approach for early software development effort estimation

Popis výsledku anglicky

Accuracy of effort estimation is one of the necessary conditions for efficiently managing software development projects. Since the information available in the early stages of software development is insufficient, software sizing metrics are considered critical factors for effort estimation. However, there is no consistent method for converting software sizing into the corresponding effort. Previous estimation methods have not considered software productivity a critical factor in estimating effort based on software sizing. This paper proposes a software productivity model based on correction factors in the Optimizing Correction Factors method through an ensemble construction mechanism of three popular machine learning techniques. The results show that using the proposed software productivity minimizes the estimation error of the methods compared to using fixed productivity metrics. © 2022, 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

Rok uplatnění

2022

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

ISBN

978-3-031-09069-1

ISSN

2367-3370

e-ISSN

2367-3389

Počet stran výsledku

13

Strana od-do

413-425

Název nakladatele

Springer International Publishing AG

Místo vydání

Basel

Místo konání akce

on-line

Datum konání akce

26. 4. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000893645700035