Genetic Programming for Cross-Release Fault Count Predictions in Large and Complex Software Projects
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InCollection{Afzal:2010:ECoaSE,
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author = "Wasif Afzal and Richard Torkar and Robert Feldt and
Tony Gorschek",
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title = "Genetic Programming for Cross-Release Fault Count
Predictions in Large and Complex Software Projects",
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booktitle = "Evolutionary Computation and Optimization Algorithms
in Software Engineering: Applications and Techniques",
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publisher = "IGI Global",
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year = "2010",
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editor = "Monica Chis",
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chapter = "6",
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pages = "94--126",
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month = jun,
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keywords = "genetic algorithms, genetic programming, SBSE",
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isbn13 = "9781615208098",
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DOI = "doi:10.4018/978-1-61520-809-8.ch006",
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abstract = "Software fault prediction can play an important role
in ensuring software quality through efficient resource
allocation. This could, in turn, reduce the potentially
high consequential costs due to faults. Predicting
faults might be even more important with the emergence
of short-timed and multiple software releases aimed at
quick delivery of functionality. Previous research in
software fault prediction has indicated that there is a
need i) to improve the validity of results by having
comparisons among number of data sets from a variety of
software, ii) to use appropriate model evaluation
measures and iii) to use statistical testing
procedures. Moreover, cross-release prediction of
faults has not yet achieved sufficient attention in the
literature. In an attempt to address these concerns,
this paper compares the quantitative and qualitative
attributes of 7 traditional and machine-learning
techniques for modelling the cross-release prediction
of fault count data. The comparison is done using
extensive data sets gathered from a total of 7
multi-release open-source and industrial software
projects. These software projects together have several
years of development and are from diverse application
areas, ranging from a web browser to a robotic
controller software. Our quantitative analysis suggests
that genetic programming (GP) tends to have better
consistency in terms of goodness of fit and accuracy
across majority of data sets. It also has comparatively
less model bias. Qualitatively, ease of configuration
and complexity are less strong points for GP even
though it shows generality and gives transparent
models. Artificial neural networks did not perform as
well as expected while linear regression gave average
predictions in terms of goodness of fit and accuracy.
Support vector machine regression and traditional
software reliability growth models performed below
average on most of the quantitative evaluation criteria
while remained on average for most of the qualitative
measures.",
- }
Genetic Programming entries for
Wasif Afzal
Richard Torkar
Robert Feldt
Tony Gorschek
Citations