Software reliability: measurement, prediction, application
Software reliability: measurement, prediction, application
Mathematics and Computers in Simulation
On the neural network approach in software reliability modeling
Journal of Systems and Software
Functional Networks with Applications: A Neural-Based Paradigm
Functional Networks with Applications: A Neural-Based Paradigm
Optimal Transformations in Multiple Linear Regression Using Functional Networks
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
AICCSA '01 Proceedings of the ACS/IEEE International Conference on Computer Systems and Applications
The optimisation of space structures using evolution strategies with functional networks
Engineering with Computers
Modeling Software Reliability Growth with Genetic Programming
ISSRE '05 Proceedings of the 16th IEEE International Symposium on Software Reliability Engineering
Functional networks training algorithm for statistical pattern recognition
ISCC '04 Proceedings of the Ninth International Symposium on Computers and Communications 2004 Volume 2 (ISCC"04) - Volume 02
Software reliability forecasting by support vector machines with simulated annealing algorithms
Journal of Systems and Software
Evaluation of Breast Cancer Tumor Classification with Unconstrained Functional Networks Classifier
AICCSA '06 Proceedings of the IEEE International Conference on Computer Systems and Applications
A first approach to solve classification problems based on functional networks
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Functional networks and the lagrange polynomial interpolation
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Software reliability models with time-dependent hazard function based on Bayesian approach
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A Bayesian predictive software reliability model with pseudo-failures
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Mathematical and Computer Modelling: An International Journal
The TAME project: towards improvement-oriented software environments
IEEE Transactions on Software Engineering
Iterative Least Squares Functional Networks Classifier
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Functional networks as a novel data mining paradigm in forecasting software development efforts
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Displacement prediction model of landslide based on functional networks
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Hi-index | 12.06 |
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.