How Multi-Objective Genetic Programming Is Effective for Software Development Effort Estimation?
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Ferrucci:2011:SSBSE,
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author = "Filomena Ferrucci and Carmine Gravino and
Federica Sarro",
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title = "How Multi-Objective Genetic Programming Is Effective
for Software Development Effort Estimation?",
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year = "2011",
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booktitle = "Search Based Software Engineering",
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editor = "Myra Cohen and Mel O'Cinneid",
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volume = "6956",
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series = "Lecture Notes in Computer Science",
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pages = "274--275",
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address = "Szeged, Hungary",
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month = "10-12 " # sep,
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, SBSE:
Poster",
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isbn13 = "978-3-642-23715-7",
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DOI = "doi:10.1007/978-3-642-23716-4_28",
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size = "1.3 page",
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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