Software Reliability Engineering with Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Yi Liu",
  title =        "Software Reliability Engineering with Genetic
  school =       "Computer Science, Florida Atlantic University",
  year =         "2003",
  address =      "Boca Raton, Florida, USA",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming, SBSE",
  URL =          "",
  broken =       "",
  size =         "243 pages",
  isbn13 =       "978-0-496-42656-0",
  abstract =     "Software reliability engineering plays a vital role in
                 managing and controlling software quality. As an
                 important method of software reliability engineering,
                 software quality estimation modelling is useful in
                 defining a cost-effective strategy to achieve a
                 reliable software system. By predicting the faults in a
                 software system, the software quality models can
                 identify high-risk modules, and thus, these high-risk
                 modules can be targeted for reliability enhancements.
                 Strictly speaking, software quality modeling not only
                 aims at lowering the misclassification rate, but also
                 takes into account the costs of different
                 misclassifications and the available resources of a
                 project. As a new search-based algorithm, Genetic
                 Programming (GP) can build a model without assuming the
                 size, shape, or structure of a model. It can flexibly
                 tailor the fitness functions to the objectives chosen
                 by the customers. Moreover, it can optimise several
                 objectives simultaneously in the modelling process, and
                 thus, a set of multi-objective optimisation solutions
                 can be obtained. This research focuses on building
                 software quality estimation models using GP. Several
                 GP-based models of predicting the class membership of
                 each software module and ranking the modules by a
                 quality factor were proposed. The first model of
                 categorising the modules into fault-prone or not
                 fault-prone was proposed by considering the
                 distinguished features of the software quality
                 classification task and GP. The second model provided
                 quality-based ranking information for fault-prone
                 modules. A decision tree-based software classification
                 model was also proposed by considering accuracy and
                 simplicity simultaneously. This new technique provides
                 a new multi-objective optimization algorithm to build
                 decision trees for real-world engineering problems, in
                 which several trade-off objectives usually have to be
                 taken into account at the same time. The fourth model
                 was built to find multi-objective optimisation
                 solutions by considering both the expected cost of
                 misclassification and available resources. Also, a new
                 goal-oriented technique of building module-order models
                 was proposed by directly optimizing several goals
                 chosen by project analysts. The issues of GP
                 , bloating and overfitting, were also addressed
                 in our research. Data were collected from three
                 industrial projects, and applied to validate the
                 performance of the models. Results indicate that our
                 proposed methods can achieve useful performance
                 results. Moreover, some proposed methods can
                 simultaneously optimize several different objectives of
                 a software project management team.",
  notes =        " page 6 Major
                 Professor: Taghi M. Khoshgoftaar

                 UMI 3095028",

Genetic Programming entries for Yi Liu