A New Software Reliability Growth Model: Genetic-Programming-Based Approach

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{journals/jsea/RahamnehRSBA11,
  author =       "Zainab {Al Rahamneh} and Mohammad Reyalat and 
                 Alaa F. Sheta and Sulieman Bani-Ahmad and Saleh Al-Oqeili",
  title =        "A New Software Reliability Growth Model:
                 Genetic-Programming-Based Approach",
  journal =      "Journal of Software Engineering and Applications",
  year =         "2011",
  number =       "8",
  volume =       "4",
  pages =        "476--481",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming, SBSE,
                 software reliability, modeling, software faults",
  URL =          "http://www.scirp.org/journal/PaperInformation.aspx?paperID=6570",
  DOI =          "doi:10.4236/jsea.2011.48054",
  size =         "6 pages",
  abstract =     "A variety of Software Reliability Growth Models (SRGM)
                 have been presented in literature. These models suffer
                 many problems when handling various types of project.
                 The reason is; the nature of each project makes it
                 difficult to build a model which can generalise. In
                 this paper we propose the use of Genetic Programming
                 (GP) as an evolutionary computation approach to handle
                 the software reliability modelling problem. GP deals
                 with one of the key issues in computer science which is
                 called automatic programming. The goal of automatic
                 programming is to create, in an automated way, a
                 computer program that enables a computer to solve
                 problems. GP will be used to build a SRGM which can
                 predict accumulated faults during the software testing
                 process. We evaluate the GP developed model and compare
                 its performance with other common growth models from
                 the literature. Our experiments results show that the
                 proposed GP model is superior compared to Yamada
                 S-Shaped, Generalised Poisson, NHPP and Schneidewind
                 reliability models.",
  bibdate =      "2011-11-08",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/jsea/jsea4.html#RahamnehRSBA11",
}

Genetic Programming entries for Zainab Al-Rahamneh Mohammad Reyalat Alaa Sheta Sulieman Bani-Ahmad Saleh Al-Oqeili

Citations