Module-Order Modeling using an Evolutionary Multi-Objective Optimization Approach
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{KhoshgoftaarLS04,
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author = "Taghi M. Khoshgoftaar and Yi Liu and Naeem Seliya",
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title = "Module-Order Modeling using an Evolutionary
Multi-Objective Optimization Approach",
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booktitle = "Proceedings of the 10th IEEE International Symposium
on Software Metrics (METRICS '04)",
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year = "2004",
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pages = "159--169",
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publisher = "IEEE Computer Society",
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keywords = "genetic algorithms, genetic programming, software
fault tolerance, software metrics, software process
improvement, module-order model, multiobjective
optimization, risk-based rankings, software faults,
software quality, software reliability improvements,
telecommunications software system",
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bibsource = "http://crestweb.cs.ucl.ac.uk/resources/sbse_repository/repository.html",
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ISSN = "1530-1435",
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DOI = "doi:10.1109/METRIC.2004.1357900",
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size = "11 pages",
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abstract = "The problem of quality assurance is important for
software systems. The extent to which software
reliability improvements can be achieved is often
dictated by the amount of resources available for the
same. A prediction for risk-based rankings of software
modules can assist in the cost-effective delegation of
the limited resources. A module-order model (MOM) is
used to gauge the performance of the predicted
rankings. Depending on the software system under
consideration, multiple software quality objectives may
be desired for a MOM; e.g., the desired rankings may be
such that if 20percent of modules were targeted for
reliability enhancements then 80percent of the faults
would be detected. In addition, it may also be desired
that if 50percent of modules were targeted then
100percent of the faults would be detected. Existing
works related to MOM(s) have used an underlying
prediction model to obtain the rankings, implying that
only the average, relative, or mean square errors are
minimized. Such an approach does not provide an insight
into the behavior of a MOM, the performance of which
focuses on how many faults are accounted for by the
given percentage of modules enhanced. We propose a
methodology for building MOM (s) by implementing a
multiobjective optimisation with genetic programming.
It facilitates the simultaneous optimisation of
multiple performance objectives for a MOM. Other
prediction techniques, e.g., multiple linear regression
and neural networks, cannot achieve multiobjective
optimisation for MOM(s). A case study of a
high-assurance telecommunications software system is
presented. The observed results show a new promise in
the modelling of goal-oriented software quality
estimation models.",
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notes = "bloat treated as multi-objective fitness Also known as
\cite{1357900}",
- }
Genetic Programming entries for
Taghi M Khoshgoftaar
Yi Liu
Jim Seliya
Citations