Grammar-based Evolutionary Approaches for Software Effort Estimation

Created by W.Langdon from gp-bibliography.bib Revision:1.8620

@InProceedings{basgalupp:2025:CEC,

• author = "Marcio P. Basgalupp and Rodrigo C. Barros and Ricardo Cerri and Ferrante Neri and Pericles B. C. Miranda and Teresa Ludermir",
• title = "Grammar-based Evolutionary Approaches for Software Effort Estimation",
• booktitle = "2025 IEEE Congress on Evolutionary Computation (CEC)",
• year = "2025",
• editor = "Yaochu Jin and Thomas Baeck",
• address = "Hangzhou, China",
• month = "8-12 " # jun,
• publisher = "IEEE",
• keywords = "genetic algorithms, genetic programming, Support vector machines, SVM, Costs, Computational modeling, Linear regression, Estimation, Evolutionary computation, Germanium, Software, Standards, software effort estimation, grammar-based genetic programming, grammatical evolution",
• isbn13 = "979-8-3315-3432-5",
• DOI = "10.1109/CEC65147.2025.11042977",
• abstract = "Software effort estimation predicts resources needed for a project, including person-hours and costs, and is vital for effective planning and budgeting. This paper compares two grammar-based evolutionary algorithms: grammar-based genetic programming (GGP) and grammatical evolution (GE). Both algorithms are tested on public project datasets and compared with machine learning models such as support vector machines, artificial neural networks, and least-squares linear regression. Results demonstrate that GGP and GE outperform alternative methods across two evaluation metrics, highlighting their effectiveness in estimating software effort.",
• notes = "also known as \cite{11042977}",

}

Genetic Programming entries for Marcio Porto Basgalupp Rodrigo C Barros Ricardo Cerri Ferrante Neri Pericles Barbosa Miranda Teresa B Ludermir

Citations