Evaluating mean squared error as a fitness function in SOMA for software effort estimation: Insights from the NASA dataset

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F24%3A63588058" target="_blank" >RIV/70883521:28140/24:63588058 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-031-70300-3_30" target="_blank" >http://dx.doi.org/10.1007/978-3-031-70300-3_30</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-70300-3_30" target="_blank" >10.1007/978-3-031-70300-3_30</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Evaluating mean squared error as a fitness function in SOMA for software effort estimation: Insights from the NASA dataset

Popis výsledku v původním jazyce

Software effort estimation plays a pivotal role in software development. The Constructive Cost Model (COCOMO) is one of the most well-known algorithmic models for estimating software effort. However, the precision of these estimates is susceptible to input constants, potentially resulting in inaccuracies. To address this challenge, this research employs the Self-Organizing Migration Algorithm (SOMA), a metaheuristic algorithm, to optimize input constants of the basic COCOMO. This study uses the NASA18 dataset to evaluate the proposed experiment&apos;s performance against the original COCOMO. Evaluation criteria such as MMRE, PRED(0.25), MAE, and MSE, with MSE serving as a fitness function, were employed to validate results. Comparative analysis indicates that optimized COCOMO estimates exhibit improved prediction accuracy. Building on these promising findings, future research will extend testing more deeply with other datasets and involve investigation of the intermediate COCOMO and COCOMO II.

Název v anglickém jazyce

Evaluating mean squared error as a fitness function in SOMA for software effort estimation: Insights from the NASA dataset

Popis výsledku anglicky

Software effort estimation plays a pivotal role in software development. The Constructive Cost Model (COCOMO) is one of the most well-known algorithmic models for estimating software effort. However, the precision of these estimates is susceptible to input constants, potentially resulting in inaccuracies. To address this challenge, this research employs the Self-Organizing Migration Algorithm (SOMA), a metaheuristic algorithm, to optimize input constants of the basic COCOMO. This study uses the NASA18 dataset to evaluate the proposed experiment&apos;s performance against the original COCOMO. Evaluation criteria such as MMRE, PRED(0.25), MAE, and MSE, with MSE serving as a fitness function, were employed to validate results. Comparative analysis indicates that optimized COCOMO estimates exhibit improved prediction accuracy. Building on these promising findings, future research will extend testing more deeply with other datasets and involve investigation of the intermediate COCOMO and COCOMO II.

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

2024

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

978-3-031-70299-0

ISSN

2367-3370

e-ISSN

2367-3389

Počet stran výsledku

13

Strana od-do

416-428

Název nakladatele

Springer Science and Business Media Deutschland GmbH

Místo vydání

Berlín

Místo konání akce

Virtual, Online

Datum konání akce

25. 4. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001413910400030