Genetic Programming for Effort Estimation: An Analysis of the Impact of Different Fitness Functions
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{FerrucciGOS10,
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author = "Filomena Ferrucci and Carmine Gravino and
Rocco Oliveto and Federica Sarro",
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title = "Genetic Programming for Effort Estimation: An Analysis
of the Impact of Different Fitness Functions",
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booktitle = "Proceedings of the 2nd International Symposium on
Search Based Software Engineering (SSBSE '10)",
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year = "2010",
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pages = "89--98",
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address = "Benevento, Italy",
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month = "7-9 " # sep,
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publisher = "IEEE",
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editor = "Massimiliano {Di Penta} and Simon Poulding and
Lionel Briand and John Clark",
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keywords = "genetic algorithms, genetic programming, SBSE",
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isbn13 = "978-0-7695-4195-2",
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DOI = "doi:10.1109/SSBSE.2010.20",
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owner = "Yuanyuan",
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timestamp = "2010.09.08",
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size = "10 pages",
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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.",
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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