Prediction And Application On Software Reliability Based On Neural Networks

With the rapid development of computer technology today, we depend more andmore on computer systems. Software is the primary factor among all that result incomputer system errors. The increasingly extended scale and complexity of softwaresystem make its reliability issue much more important. So how to secure the qualityof software as well as design and develop reliable software is our urgent task. As theprediction of software reliability enables the developers and testers to get generalideas about the software reliability before testing and using it, it is important forfurther development, testing and quality control of software.Neural Network (NN) has been deeply appreciated by many scholars. Now it isused with great success in many fields. Prediction is one of the important applicationfields of NN. Most of the general predicting methods are based on linear analysis,when it comes to non-linear they met many difficulties. Therefore NN is competentfor non-linear proceeding for its excellent non-linear character. Predicting methodsbased on NN extend the space of predicting research.This paper summarizes the status of software reliability prediction methods inhome and abroad. Through research and analysis of traditional software reliabilitymodels and neural network structure and characteristics we pointed out theirdeficiencies. The low speed of learning is a main problem for the BP neural networksin fault diagnosis systems. On the basis of others theory and results of softwarereliability prediction on neural network, the momentum term and variable studyingrate are increased to the standard BP algorithm, and LM algorithm is used to train theneural network because the algorithm can solve problem of velocity effectively, anddesigning Optimally of the network structure and numbers of the hidden layer nodes.We come up with the better software reliability prediction model based on ANN withthe help of many mathematic tools such as the Zone-Probing method, the Searchmethod gradually, data scaling, cross validation and so on.At last, Simulation shows that compared with the classic models, the new modelhas better prediction precision, better generalization ability and lower dependence on the number of samples.for the further verification of this model we apply it to somecellular phone software to predict the software reliability of it.