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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

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

Name of the periodical

IEEE Access

ISSN

2169-3536

e-ISSN

2169-3536

Volume of the periodical

11

Issue of the periodical within the volume

Neuveden

Country of publishing house

US - UNITED STATES

Number of pages

17

Pages from-to

126335-126351

UT code for WoS article

001111143300001

EID of the result in the Scopus database

2-s2.0-85177567332