A grammatical evolution approach for software effort estimation

Created by W.Langdon from gp-bibliography.bib Revision:1.7964

@InProceedings{Barros:2013:GECCO,

• author = "Rodrigo C. Barros and Marcio P. Basgalupp and Ricardo Cerri and Tiago S. {da Silva} and Andre C. P. L. F. {de Carvalho}",
• title = "A grammatical evolution approach for software effort estimation",
• booktitle = "GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference",
• year = "2013",
• editor = "Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi",
• isbn13 = "978-1-4503-1963-8",
• pages = "1413--1420",
• keywords = "genetic algorithms, genetic programming",
• month = "6-10 " # jul,
• organisation = "SIGEVO",
• address = "Amsterdam, The Netherlands",
• DOI = "doi:10.1145/2463372.2463546",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• abstract = "Software effort estimation is an important task within software engineering. It is widely used for planning and monitoring software project development as a means to deliver the product on time and within budget. Several approaches for generating predictive models from collected metrics have been proposed throughout the years. Machine learning algorithms, in particular, have been widely-employed to this task, bearing in mind their capability of providing accurate predictive models for the analysis of project stakeholders. In this paper, we propose a grammatical evolution approach for software metrics estimation. Our novel algorithm, namely SEEGE, is empirically evaluated on public project data sets, and we compare its performance with state-of-the-art machine learning algorithms such as support vector machines for regression and artificial neural networks, and also to popular linear regression. Results show that SEEGE outperforms the other algorithms considering three different evaluation measures, clearly indicating its effectiveness for the effort estimation task.",
• notes = "Also known as \cite{2463546} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)",

}

Genetic Programming entries for Rodrigo C Barros Marcio Porto Basgalupp Ricardo Cerri Tiago S da Silva Andre Ponce de Leon F de Carvalho

Citations