id: 06065837
dt: a
an: 06065837
au: Benala, Tirimula Rao; Dehuri, Satchidananda; Satapathy, Suresh Chandra;
    Raghavi, Ch.Sudha
ti: Genetic algorithm for optimizing neural network based software cost
    estimation.
so: Panigrahi, Bijaya Ketan (ed.) et al., Swarm, evolutionary, and memetic
    computing. Second international conference, SEMCCO 2011, Visakhapatnam,
    Andhra Pradesh, India, December 19‒21, 2011. Proceedings, Part I.
    Berlin: Springer (ISBN 978-3-642-27171-7/pbk). Lecture Notes in
    Computer Science 7076, 233-239 (2011).
py: 2011
pu: Berlin: Springer
la: EN
cc: 
ut: software cost estimation; genetic algorithm; ANN; BP-Learning; and
    COCOMO-II
ci: 
li: doi:10.1007/978-3-642-27172-4_29
ab: Summary: Software engineering cost models and estimation techniques are
    used for number of purposes. These include budgeting, tradeoff and risk
    analysis, project planning and control, software improvement and
    investment analysis. The proposed work uses neural network based
    estimation, which is essentially a machine learning approach, is one of
    the most popular techniques. In this paper the author has proposed a 2
    step process for software effort prediction. In first phase known as
    training phase neural network selects the matching class (datasets) for
    the given input, which is improved by optimizing the parameters of each
    individual dataset by Genetic algorithm. In second step known as
    testing phase, the prediction process is done by adaptive neural
    networks. The proposed method uses COCOMO-II as base model. The
    experimental results show that our method could significantly improve
    prediction accuracy of conventional Artificial Neural Networks (ANN)
    and has potential to become an effective method for software cost
    estimation.
rv: