Evolutionary Optimization of Software Quality Modeling with Multiple Repositories
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Liu:2010:ieeeTSE,
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author = "Yi (Cathy) Liu and Taghi M. Khoshgoftaar and
Naeem Seliya",
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title = "Evolutionary Optimization of Software Quality Modeling
with Multiple Repositories",
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journal = "IEEE Transactions on Software Engineering",
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year = "2010",
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month = nov # "/" # dec,
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volume = "36",
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number = "6",
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pages = "852--864",
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keywords = "genetic algorithms, genetic programming, sbse,
baseline classifier, evolutionary optimisation, machine
learner, multiple software project repository, robust
software quality model, search-based software quality
model, software data set, software measurement data
repository, software metrics, software quality
modelling, validation classifier, validation-and-voting
classifier, software management, software metrics,
software quality",
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ISSN = "0098-5589",
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DOI = "doi:10.1109/TSE.2010.51",
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abstract = "A novel search-based approach to software quality
modelling with multiple software project repositories
is presented. Training a software quality model with
only one software measurement and defect data set may
not effectively encapsulate quality trends of the
development organisation. The inclusion of additional
software projects during the training process can
provide a cross-project perspective on software quality
modelling and prediction. The genetic-programming-based
approach includes three strategies for modeling with
multiple software projects: Baseline Classifier,
Validation Classifier, and Validation-and-Voting
Classifier. The latter is shown to provide better
generalisation and more robust software quality models.
This is based on a case study of software metrics and
defect data from seven real-world systems. A second
case study considers 17 different (nonevolutionary)
machine learners for modelling with multiple software
data sets. Both case studies use a similar
majority-voting approach for predicting fault-proneness
class of program modules. It is shown that the total
cost of misclassification of the search-based software
quality models is consistently lower than those of the
non-search-based models. This study provides clear
guidance to practitioners interested in exploiting
their organization's software measurement data
repositories for improved software quality modelling.",
-
notes = "Also known as \cite{5467094}",
- }
Genetic Programming entries for
Yi Liu
Taghi M Khoshgoftaar
Jim Seliya
Citations