Using Genetic Programming to Improve Software Effort Estimation Based on General Data Sets
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @InProceedings{lefley:2003:gecco,
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author = "Martin Lefley and Martin J. Shepperd",
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title = "Using Genetic Programming to Improve Software Effort
Estimation Based on General Data Sets",
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booktitle = "Genetic and Evolutionary Computation -- GECCO-2003",
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editor = "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and
D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and
R. Standish and G. Kendall and S. Wilson and
M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and
A. C. Schultz and K. Dowsland and N. Jonoska and
J. Miller",
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year = "2003",
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pages = "2477--2487",
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address = "Chicago",
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publisher_address = "Berlin",
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month = "12-16 " # jul,
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volume = "2724",
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series = "LNCS",
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ISBN = "3-540-40603-4",
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publisher = "Springer-Verlag",
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keywords = "genetic algorithms, genetic programming, Search Based
Software Engineering",
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DOI = "doi:10.1007/3-540-45110-2_151",
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abstract = "various techniques including genetic programming, with
public data sets, to attempt to model and hence
estimate software project effort. The main research
question is whether genetic programs can offer `better'
solution search using public domain metrics rather than
company specific ones. Unlike most previous research, a
realistic approach is taken, whereby predictions are
made on the basis of the data available at a given
date. Experiments are reported, designed to assess the
accuracy of estimates made using data within and beyond
a specific company. This research also offers insights
into genetic programming's performance, relative to
alternative methods, as a problem solver in this
domain. The results do not find a clear winner but, for
this data, GP performs consistently well, but is harder
to configure and produces more complex models. The
evidence here agrees with other researchers that
companies would do well to base estimates on in house
data rather than incorporating public data sets. The
complexity of the GP must be weighed against the small
increases in accuracy to decide whether to use it as
part of any effort prediction estimation.",
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notes = "GECCO-2003. A joint meeting of the twelfth
International Conference on Genetic Algorithms
(ICGA-2003) and the eighth Annual Genetic Programming
Conference (GP-2003)",
- }
Genetic Programming entries for
Martin Lefley
Martin J Shepperd
Citations