How Multi-Objective Genetic Programming Is Effective for Software Development Effort Estimation?

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Ferrucci:2011:SSBSE,

• author = "Filomena Ferrucci and Carmine Gravino and Federica Sarro",
• title = "How Multi-Objective Genetic Programming Is Effective for Software Development Effort Estimation?",
• year = "2011",
• booktitle = "Search Based Software Engineering",
• editor = "Myra Cohen and Mel O'Cinneid",
• volume = "6956",
• series = "Lecture Notes in Computer Science",
• pages = "274--275",
• address = "Szeged, Hungary",
• month = "10-12 " # sep,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, SBSE: Poster",
• isbn13 = "978-3-642-23715-7",
• DOI = "doi:10.1007/978-3-642-23716-4_28",
• size = "1.3 page",
• abstract = "The idea of exploiting search-based methods to estimate development effort is based on the observation that the effort estimation problem can be formulated as an optimisation problem. As a matter of fact, among possible estimation models, we have to identify the best one, i.e., the one providing the most accurate estimates. Nevertheless, in the context of effort estimation there does not exist a unique measure that allows us to compare different models and consistently derives the best one [1]. Rather, several evaluation criteria (e.g., MMRE and Pred(25)) covering different aspects of model performances (e.g., underestimating or overestimating) are used to assess and compare estimation models [1]. Thus, considering the effort estimation problem as an optimisation problem we should search for the model that optimises several measures. From this point of view, the effort estimation problem is inherently multi-objective. Nevertheless, all the studies that have been carried",

}

Genetic Programming entries for Filomena Ferrucci Carmine Gravino Federica Sarro

Citations