Genetic Programming for Effort Estimation: An Analysis of the Impact of Different Fitness Functions

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@InProceedings{FerrucciGOS10,

• author = "Filomena Ferrucci and Carmine Gravino and Rocco Oliveto and Federica Sarro",
• title = "Genetic Programming for Effort Estimation: An Analysis of the Impact of Different Fitness Functions",
• booktitle = "Proceedings of the 2nd International Symposium on Search Based Software Engineering (SSBSE '10)",
• year = "2010",
• pages = "89--98",
• address = "Benevento, Italy",
• month = "7-9 " # sep,
• publisher = "IEEE",
• editor = "Massimiliano {Di Penta} and Simon Poulding and Lionel Briand and John Clark",
• keywords = "genetic algorithms, genetic programming, SBSE",
• isbn13 = "978-0-7695-4195-2",
• DOI = "doi:10.1109/SSBSE.2010.20",
• owner = "Yuanyuan",
• timestamp = "2010.09.08",
• size = "10 pages",
• abstract = "Context: The use of search-based methods has been recently proposed for software development effort estimation and some case studies have been carried out to assess the effectiveness of Genetic Programming (GP). The results reported in the literature showed that GP can provide an estimation accuracy comparable or slightly better than some widely used techniques and encouraged further research to investigate whether varying the fitness function the estimation accuracy can be improved. Aim: Starting from these considerations, in this paper we report on a case study aiming to analyse the role played by some fitness functions for the accuracy of the estimates. Method: We performed a case study based on a publicly available dataset, i.e., Desharnais, by applying a 3-fold cross validation and employing summary measures and statistical tests for the analysis of the results. Moreover, we compared the accuracy of the obtained estimates with those achieved using some widely used estimation methods, namely Case-Based Reasoning (CBR) and Manual Step Wise Regression (MSWR). Results: The obtained results highlight that the fitness function choice significantly affected the estimation accuracy. The results also revealed that GP provided significantly better estimates than CBR and comparable with those of MSWR for the considered dataset.",
• notes = "IEEE Computer Society Order Number P4195 BMS Part Number: CFP1099G-PRT Library of Congress Number 2010933544 http://ssbse.info/2010/program.php",

}

Genetic Programming entries for Filomena Ferrucci Carmine Gravino Rocco Oliveto Federica Sarro

Citations