Enhancing Software Effort Estimation With Self-Organizing Migration Algorithm: A Comparative Analysis of COCOMO Models
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F24%3A63587898" target="_blank" >RIV/70883521:28140/24:63587898 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/10526276" target="_blank" >https://ieeexplore.ieee.org/document/10526276</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ACCESS.2024.3399060" target="_blank" >10.1109/ACCESS.2024.3399060</a>
Alternative languages
Result language
angličtina
Original language name
Enhancing Software Effort Estimation With Self-Organizing Migration Algorithm: A Comparative Analysis of COCOMO Models
Original language description
This study presents a comprehensive analysis of enhancing software effort estimation accuracy using a Self-Organizing Migration Algorithm (SOMA)-optimized Constructive Cost Model (COCOMO). By conducting a comparative study of traditional COCOMO models and SOMA-optimized variants across preprocessed datasets (NASA93, NASA63, NASA18, Kemerer, Miyazaki94, and Turkish), our research focuses on crucial evaluation metrics, including Mean Magnitude of Relative Error (MMRE), Prediction at 0.25 (PRED(0.25)), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The analysis encompasses various configurations of COCOMO models-basic, intermediate, and post-architecture COCOMO II, supplemented with additional statistical testing and residual analysis for in-depth insights. The results demonstrate that the SOMA-optimized COCOMO models generally surpass traditional models in predictive accuracy, especially notable in metrics such as MMRE where an improvement of up to 12%, PRED(0.25) with an enhancement of 15%, MAE reduction by 18%, and a decrease in RMSE by 20% were observed. However, performance variances were identified in specific scenarios, highlighting areas for further refinement, particularly in large-scale estimations where residual plots suggested the potential for underestimation or overestimation. The study concludes that integrating the SOMA optimization algorithm into COCOMO models significantly enhances the accuracy of software effort estimations, providing valuable insights for future research to optimise estimations for larger projects and advance prediction models. This advancement addresses the technical challenge of parameter accuracy and offers a methodological improvement in model selection and application, underscoring the potential of metaheuristic optimization in software effort estimation.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - 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)
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2024
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
12
Issue of the periodical within the volume
Neuveden
Country of publishing house
US - UNITED STATES
Number of pages
19
Pages from-to
67170-67188
UT code for WoS article
001226070500001
EID of the result in the Scopus database
2-s2.0-85193012866