Efficient software development effort estimation approaches for improving scalability in the training phase
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F25%3A63597678" target="_blank" >RIV/70883521:28140/25:63597678 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/11071673" target="_blank" >https://ieeexplore.ieee.org/document/11071673</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ACCESS.2025.3586081" target="_blank" >10.1109/ACCESS.2025.3586081</a>
Alternative languages
Result language
angličtina
Original language name
Efficient software development effort estimation approaches for improving scalability in the training phase
Original language description
Effective software effort estimation is essential for project management, but faces scalability challenges with large datasets. While clustering can address this complexity, standard methods often rely on random initial centers, leading to inconsistent and less precise results. This randomness frequently overlooks critical contextual factors, such as industry or domain-specific characteristics, which can impair cluster quality and the accuracy of effort predictions. To overcome these issues, this study introduces the Contextual Initial Cluster Centroids (CICC), a novel methodology designed to optimize initial centroid selection. Unlike approaches that depend on randomness or are computationally intensive, CICC uses parallel processing of Jaccard similarity and a refined neighbor-finding technique, K-Reciprocal Nearest Neighbors (KRNN), to identify the most relevant and similar projects as initial centers. This deterministic approach ensures clusters are built around meaningful, context-rich representatives, reducing computations and improving scalability. Experiments on public software project datasets show that CICC significantly outperforms existing techniques. It achieves higher cluster quality, measured by metrics like the Global Silhouette Index, and provides more accurate effort estimates, indicated by lower MAE and higher PRED values. By establishing a more robust and efficient foundation for effort estimation, CICC offers considerable potential to optimize project planning and resource allocation in large-scale software development.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Access
ISSN
2169-3536
e-ISSN
2169-3536
Volume of the periodical
13
Issue of the periodical within the volume
Neuveden
Country of publishing house
US - UNITED STATES
Number of pages
20
Pages from-to
116304-116323
UT code for WoS article
001527231900008
EID of the result in the Scopus database
2-s2.0-105010027211