Evolutionary Computing Driven Search Based Software Testing and Correction

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Joshua Lee Wilkerson",
  title =        "Evolutionary Computing Driven Search Based Software
                 Testing and Correction",
  school =       "Computer Science, Missouri University of Science and
  year =         "2012",
  address =      "USA",
  keywords =     "genetic algorithms, genetic programming, SBSE",
  URL =          "http://hdl.handle.net/10355/26508",
  URL =          "https://mospace.umsystem.edu/xmlui/bitstream/handle/10355/26508/Wilkerson_2012.pdf",
  size =         "160 pages",
  abstract =     "For a given program, testing, locating the errors
                 identified, and correcting those errors is a critical,
                 yet expensive process. The field of Search Based
                 Software Engineering (SBSE) addresses these phases by
                 formulating them as search problems. This dissertation
                 addresses these challenging problems through the use of
                 two complimentary evolutionary computing based systems.
                 The first one is the Fitness Guided Fault Localisation
                 (FGFL) system, which novelly uses a specification based
                 fitness function to perform fault localisation. The
                 second is the Coevolutionary Automated Software
                 Correction (CASC) system, which employs a variety of
                 evolutionary computing techniques to perform testing,
                 correction, and verification of software. In support of
                 the real world application of these systems, a
                 practitioner's guide to fitness function design is

                 For the FGFL system, experimental results are presented
                 that demonstrate the applicability of fitness guided
                 fault localisation to automate this important phase of
                 software correction in general, and the potential of
                 the FGFL system in particular. For the fitness function
                 design guide, the performance of a guide generated
                 fitness function is compared to that of an expert
                 designed fitness function demonstrating the
                 competitiveness of the guide generated fitness
                 function. For the CASC system, results are presented
                 that demonstrate the system's abilities on a series of
                 problems of both increasing size as well as number of
                 bugs present. The system presented solutions more than
                 90percent of the time for versions of the programs
                 containing one or two bugs. Additionally, scalability
                 results are presented for the CASC system that indicate
                 that success rate linearly decreases with problem size
                 and that the estimated convergence rate scales at worst
                 linearly with problem size.",
  notes =        "Supervisor: Dr. Daniel Tauritz

                 Rest of committee Dr. Thomas Weigert Dr. Bruce McMillin
                 Dr. Ali Hurson Dr. Sahra Sedighsarvestani",

Genetic Programming entries for Josh L Wilkerson