Can genetic programming improve software effort estimation? A comparative evaluation
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Burgess:2001:IST,
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author = "Colin J. Burgess and Martin Lefley",
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title = "Can genetic programming improve software effort
estimation? A comparative evaluation",
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year = "2001",
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journal = "Information and Software Technology",
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volume = "43",
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number = "14",
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pages = "863--873",
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month = "15 " # dec,
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keywords = "genetic algorithms, genetic programming, Case-based
reasoning, Machine learning, Neural networks, Software
effort estimation",
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URL = "http://www.sciencedirect.com/science/article/B6V0B-44D4196-7/1/20f45986fc0a4827ad09169178379d73",
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DOI = "doi:10.1016/S0950-5849(01)00192-6",
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abstract-url = "http://www.cs.bris.ac.uk/Publications/pub_info.jsp?id=1000586",
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size = "11 pages",
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abstract = "Accurate software effort estimation is an important
part of the software process. Originally, estimation
was performed using only human expertise, but more
recently, attention has turned to a variety of machine
learning (ML) methods. This paper attempts to evaluate
critically the potential of genetic programming (GP) in
software effort estimation when compared with
previously published approaches, in terms of accuracy
and ease of use. The comparison is based on the
well-known Desharnais data set of 81 software projects
derived from a Canadian software house in the late
1980s. The input variables are restricted to those
available from the specification stage and significant
effort is put into the GP and all of the other solution
strategies to offer a realistic and fair comparison.
There is evidence that GP can offer significant
improvements in accuracy but this depends on the
measure and interpretation of accuracy used. GP has the
potential to be a valid additional tool for software
effort estimation but set up and running effort is high
and interpretation difficult, as it is for any complex
meta-heuristic technique.",
- }
Genetic Programming entries for
Colin J Burgess
Martin Lefley
Citations