A Multiobjective Module-Order Model for Software Quality Enhancement

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Taghi M. Khoshgoftaar and Yi Liu and Naeem Seliya",
  title =        "A Multiobjective Module-Order Model for Software
                 Quality Enhancement",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2004",
  volume =       "8",
  number =       "6",
  pages =        "593--608",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, module-order
                 model (MOM), multiobjective optimization (MOO),
                 software metrics, software quality estimation, SBSE",
  bibsource =    "http://crestweb.cs.ucl.ac.uk/resources/sbse_repository/repository.html",
  ISSN =         "1089-778X",
  DOI =          "doi:10.1109/TEVC.2004.837108",
  size =         "16 pages",
  abstract =     "The knowledge, prior to system operations, of which
                 program modules are problematic is valuable to a
                 software quality assurance team, especially when there
                 is a constraint on software quality enhancement
                 resources. A cost-effective approach for allocating
                 such resources is to obtain a prediction in the form of
                 a quality-based ranking of program modules.
                 Subsequently, a module-order model (MOM) is used to
                 gauge the performance of the predicted rankings. From a
                 practical software engineering point of view, multiple
                 software quality objectives may be desired by a MOM for
                 the system under consideration: e.g., the desired
                 rankings may be such that 100percent of the faults
                 should be detected if the top 50percent of modules with
                 highest number of faults are subjected to quality
                 improvements. Moreover, the management team for the
                 same system may also desire that 80percent of the
                 faults should be accounted if the top 20percent of the
                 modules are targeted for improvement.

                 Existing work related to MOM(s) use a quantitative
                 prediction model to obtain the predicted rankings of
                 program modules, implying that only the fault
                 prediction error measures such as the average,
                 relative, or mean square errors are minimized. Such an
                 approach does not provide a direct insight into the
                 performance behavior of a MOM. For a given percentage
                 of modules enhanced, the performance of a MOM is gauged
                 by how many faults are accounted for by the predicted
                 ranking as compared with the perfect ranking. We
                 propose an approach for calibrating a multi-objective
                 MOM using genetic programming. Other estimation
                 techniques, e.g., multiple linear regression and neural
                 networks cannot achieve multi objective optimization
                 for MOM(s). The proposed methodology facilitates the
                 simultaneous optimization of multiple performance
                 objectives for a MOM. Case studies of two industrial
                 software systems are presented, the empirical results
                 of which demonstrate a new promise for goal-oriented
                 software quality modeling.",
  notes =        "lilgp. Also known as \cite{1369249}",

Genetic Programming entries for Taghi M Khoshgoftaar Yi Liu Jim Seliya