Evaluating kernel functions in software effort estimation: A comparative study of moving window and spectral clustering models across diverse datasets

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F23%3A63570958" target="_blank" >RIV/70883521:28140/23:63570958 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10304119" target="_blank" >https://ieeexplore.ieee.org/document/10304119</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2023.3329369" target="_blank" >10.1109/ACCESS.2023.3329369</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Evaluating kernel functions in software effort estimation: A comparative study of moving window and spectral clustering models across diverse datasets

Popis výsledku v původním jazyce

This study embarks on an in-depth analysis of the performance of various kernel functions, namely uniform, epanechnikov, triangular, and gaussian, in window-based and spectral clustering-based models. Employing seven distinct datasets, our approach evaluated both window sizes (25%, 50%, 75%, and 100%) and clustering clusters (ranging from 1 to 4). The kernel functions served as weighting functions for regression models, leading to the creation of 192 window-based and 192 clustering-based models. Our analysis underscores the dominance of the uniform kernel function. In most models where the Pred(0.25) was maximal and the Mean Absolute Error was minimal, the uniform kernel function was predominantly utilized. Further, our results exhibit varying outcomes between moving windows and spectral clustering across datasets. For instance, in the fpa-china dataset, while moving windows with a 50% size displayed no significant superiority over spectral-clustering with 1 cluster, spectral-clustering (1 cluster) demonstrated a significantly enhanced performance. However, in datasets like fpa-kitchenham, neither approach proved to be significantly better. This comprehensive exploration into the efficiency of kernel functions in moving windows and spectral-clustering models provides valuable insights for future research and applications in data modelling and analysis.

Název v anglickém jazyce

Evaluating kernel functions in software effort estimation: A comparative study of moving window and spectral clustering models across diverse datasets

Popis výsledku anglicky

This study embarks on an in-depth analysis of the performance of various kernel functions, namely uniform, epanechnikov, triangular, and gaussian, in window-based and spectral clustering-based models. Employing seven distinct datasets, our approach evaluated both window sizes (25%, 50%, 75%, and 100%) and clustering clusters (ranging from 1 to 4). The kernel functions served as weighting functions for regression models, leading to the creation of 192 window-based and 192 clustering-based models. Our analysis underscores the dominance of the uniform kernel function. In most models where the Pred(0.25) was maximal and the Mean Absolute Error was minimal, the uniform kernel function was predominantly utilized. Further, our results exhibit varying outcomes between moving windows and spectral clustering across datasets. For instance, in the fpa-china dataset, while moving windows with a 50% size displayed no significant superiority over spectral-clustering with 1 cluster, spectral-clustering (1 cluster) demonstrated a significantly enhanced performance. However, in datasets like fpa-kitchenham, neither approach proved to be significantly better. This comprehensive exploration into the efficiency of kernel functions in moving windows and spectral-clustering models provides valuable insights for future research and applications in data modelling and analysis.

Druh

J_imp - Článek v periodiku v databázi Web of Science

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 periodika

IEEE Access

ISSN

2169-3536

e-ISSN

2169-3536

Svazek periodika

11

Číslo periodika v rámci svazku

Neuveden

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

17

Strana od-do

126335-126351

Kód UT WoS článku

001111143300001

EID výsledku v databázi Scopus

2-s2.0-85177567332