Software reliability identification using functional networks: A comparative study

Software engineering development has gradually become essential element in different aspects of the daily life and an important factor in numerous critical real-industry applications, such as, nuclear plants, medical monitoring control, real-time military, bioinformatics, oil and gas industry, and air traffic control. This paper proposes a functional network as a novel computational intelligence scheme for tracking and predicting the software reliability. Several applications are presented to illustrate this new intelligent system framework models. To demonstrate the usefulness of functional networks and the existing data mining schemes, we briefly describe the learning algorithm of functional networks associativity model in predicting the software reliability. Comparative studies will be carried out to compare the performance of functional networks with the most popular existing data mining techniques, such as, statistical regression multilayer feed forward neural networks, and support vector machines. The results show that the performance of functional networks is more reliable, stable, accurate, and outperforms other techniques.