Analysis of Software Engineering Data Using Computational Intelligence Techniques

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

@InProceedings{DBLP:conf/oois/JarilloSPR01,
  author =       "Gabriel Jarillo and Giancarlo Succi and 
                 Witold Pedrycz and Marek Reformat",
  title =        "Analysis of Software Engineering Data Using
                 Computational Intelligence Techniques",
  booktitle =    "7th International Conference on Object Oriented
                 Information Systems, OOIS'2001",
  year =         "2001",
  editor =       "Yingxu Wang and Shushma Patel and Ronald Johnston",
  pages =        "133--142",
  address =      "Calgary, Canada",
  month =        "27-29 " # aug,
  publisher =    "Springer",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  keywords =     "genetic algorithms, genetic programming, SBSE",
  isbn13 =       "9781852335465",
  URL =          "http://www.springer.com/computer/database+management+%26+information+retrieval/book/978-1-85233-546-5",
  URL =          "http://www.inf.unibz.it/~gsucci/publications/images/analysisofsoftwareengineeringdatausingcomputationalsoftwaretechniques.pdf",
  size =         "10 pages",
  abstract =     "The accurate estimation of software development effort
                 has major implications for the management of software
                 development in the industry. Underestimates lead to
                 time pressures that may compromise full functional
                 development and thorough testing of the software
                 product. On the other hand, overestimates can result in
                 over allocation of development resources and personnel
                 [7]. Many models for effort estimation have been
                 developed during the past years; some of them use
                 parametric methods with some degree of success, other
                 kind of methods belonging to the computational
                 intelligence family, such as Neural Networks (NN), have
                 been also studied in this field showing more accurate
                 estimations, and finally the Genetic programming (GP)
                 techniques are being considered as promising tools for
                 the prediction of effort estimation.

                 Organizations are wandering how they can predict the
                 quality of their software before it is used. Generally
                 there are tree approaches to do so [1]:

                 1. - Predicting the number of defects in the system.

                 2. - Estimating the reliability of the system in terms
                 of time and failure.

                 3. - Understanding the impact of the design and testing
                 processes on defect counts and failure
                 densities.

                 Knowing the quality of the software allows the
                 organization to estimate the amount of resources to be
                 invested on its maintenance. Software maintenance is a
                 factor that consumes most of the resources in many
                 software organizations [2], therefore its worth it to
                 be able to characterize, assess and predict defects in
                 the software at early stages of its development in
                 order to reduce maintenance costs. Maintenance involves
                 activities such as correcting errors, maintaining
                 software, and adapting software to deal with new
                 environment requirements [2].",
  notes =        "http://enel.ucalgary.ca/oois2001/programme.html",
}

Genetic Programming entries for Gabriel Jarillo Giancarlo Succi Witold Pedrycz Marek Reformat

Citations