CONTENTS

SOFTWARE COST ESTIMATION MODEL BASED ON NEURAL NETWORKS 209
based on a type of Mean Square Error [8], or when a certain number of learning
epochs has been performed.
After stopping the learning process we have to measure the quality of the trained
neural network. For this step we will use a validation set from the training set that
was not presented in the learning phase to the neural network. If, the validation phase
is producing an acceptable result, then the neural network can be used in production
for cost estimations of real life software development projects.
Because the COCOMO [18] training data set contains some attributes like person/months
that have a high variation we have applied a logarithmic transformation
in order to normalize such attributes.
4.3. Experiments and simulations. We have implemented a neural network of
Multi Layer Perceptron type with 2 hidden layers and we have applied an enhanced
Back Propagation learning algorithm. Based on this implementation, after the learning
and validation phase, we were able to perform realistic cost estimation for software
development projects. During these experiments and simulation we have varied the
parameters that are influencing the learning process in order to obtain the most efficient
neural network model. The parameters that have been taken into account
where: dimensionality of the training set, learning rate, number of neurons in the
hidden layers, number of epochs [10].
The simulations where performed using the COCOMO [18] dataset as training
set. We have randomly chosen 40 projects to be included in the learning phase and
the rest of 23 projects have been used for validation.
5. Conclusions
We have applied a new model based on neural networks for the cost estimation
of the software development projects. The obtained results are promising and can
be an important tool for project management of software development [14]. Based
on the fundamental ability of the neural networks to learn from a historical training
data set, we can use the experience contained in previously successful or unsuccessful
estimations to make new reliable software cost estimations for software development
projects.
References
[1] Al-Sakran H., Software cost estimation model based on integration of multi-agent and casebased
reasoning. Journal of Computer Science, pag. 276-278, (2006).
[2] Albrecht A., Gaffney J. Jr., Software function, source lines of code, and development effort
prediction: A software science validation. IEEE Trans. Software Eng., vol. 9, pp. 639-648,
(1983).
[3] Boehm B., Software engineering economics, Englewood Cliffs, NJ:Prentice-Hall, ISBN 0-13-
822122-7, (1981).
[4] Boehm B., et al., Software cost estimation with COCOMO II, Englewood Cliffs, NJ:Prentice-
Hall, ISBN 0-13-026692-2, (2000).
[5] Cantone G., Cimitile A., De Carlini U., A comparison of models for software cost estimation
and management of software projects. Computer Systems: Performance and Simulation,
Elsevier Science Publishers B.V., (1986).