Module-Order Modeling using an Evolutionary Multi-Objective Optimization Approach

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{KhoshgoftaarLS04,
  author =       "Taghi M. Khoshgoftaar and Yi Liu and Naeem Seliya",
  title =        "Module-Order Modeling using an Evolutionary
                 Multi-Objective Optimization Approach",
  booktitle =    "Proceedings of the 10th IEEE International Symposium
                 on Software Metrics (METRICS '04)",
  year =         "2004",
  pages =        "159--169",
  publisher =    "IEEE Computer Society",
  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",
  bibsource =    "http://crestweb.cs.ucl.ac.uk/resources/sbse_repository/repository.html",
  ISSN =         "1530-1435",
  DOI =          "doi:10.1109/METRIC.2004.1357900",
  size =         "11 pages",
  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.",
  notes =        "bloat treated as multi-objective fitness Also known as
                 \cite{1357900}",
}

Genetic Programming entries for Taghi M Khoshgoftaar Yi Liu Jim Seliya

Citations