On the application of genetic programming for software engineering predictive modeling: A systematic review
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- @Article{Afzal201111984,
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author = "Wasif Afzal and Richard Torkar",
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title = "On the application of genetic programming for software
engineering predictive modeling: A systematic review",
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journal = "Expert Systems with Applications",
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volume = "38",
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number = "9",
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pages = "11984--11997",
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year = "2011",
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ISSN = "0957-4174",
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DOI = "doi:10.1016/j.eswa.2011.03.041",
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URL = "http://www.sciencedirect.com/science/article/B6V03-52C8FT6-5/2/668361024e4b2bcf9a4a73195271591c",
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keywords = "genetic algorithms, genetic programming, Systematic
review, Symbolic regression, Modelling",
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abstract = "The objective of this paper is to investigate the
evidence for symbolic regression using genetic
programming (GP) being an effective method for
prediction and estimation in software engineering, when
compared with regression/machine learning models and
other comparison groups (including comparisons with
different improvements over the standard GP algorithm).
We performed a systematic review of literature that
compared genetic programming models with comparative
techniques based on different independent project
variables. A total of 23 primary studies were obtained
after searching different information sources in the
time span 1995-2008. The results of the review show
that symbolic regression using genetic programming has
been applied in three domains within software
engineering predictive modeling: (i) Software quality
classification (eight primary studies). (ii) Software
cost/effort/size estimation (seven primary studies).
(iii) Software fault prediction/software reliability
growth modelling (eight primary studies). While there
is evidence in support of using genetic programming for
software quality classification, software fault
prediction and software reliability growth modelling;
the results are inconclusive for software
cost/effort/size estimation.",
- }
Genetic Programming entries for
Wasif Afzal
Richard Torkar
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