MEASURING SOFTWARE COMPLEXITY USING NEURAL NETWORKS

Yıl 2017, Cilt: 17 Sayı: 2, 3503 - 3508, 27.07.2017

Sibel Senan , Selçuk Sevgen

Öz

Measuring the software complexity is an important task
in the management of software projects. In the recent years, many researchers
have paid much attention to this challenging task due to the commercial
importance of software projects. In the literature, there are some software
metrics and estimation models to measure the complexity of software. However, we
still need to introduce novel models of software metrics to obtain more
accurate results regarding software complexity.  In this paper, we will show that neural networks
can be used as an  alternative  method for estimation of software complexity
metrics. We use a neural network of three layers with a single hidden layer and
train this network by using distinct training algorithms to determine the
accuracy of software complexity. We compare our results of software complexity obtained by
using neural networks with those calculated by Halstead model.  This
comparison shows that the difference between our estimated results obtained by Bayesian Regularization
Algorithm with 10 hidden neurons and Halstead calculated results of software
complexity is less than 2%, implying the effectiveness of our proposed method
of neural networks in estimating software complexity. 

Anahtar Kelimeler

Software Complexity, Neural Networks, Halstead Metrics

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