Prediction of software reliability using connectionist models

Applying Neural Network Approach with Imperialist Competitive Algorithm for Software Reliability Prediction

“…In existing techniques researchers exploit single-input single-output neural network structure to develop the software reliability models. Cumulative execution time as the input and the number of failures as the output have been considered by Karunanithi et al, in [2]. On the other hand, [5] set the number of failures as input and the time of failure as the output.…”

“…Karunanithi et al [2] proposed the first NN model of software reliability prediction. Two other models were suggested by Adnan et al [3] and Park et al [4] based on using neural networks, and their results showed the effectiveness of their approach compared with analytical models.…”

Support Vector Regression for Software Reliability Growth Modeling and Prediction

“…Two thirds of the samples are randomly drawn from the original data set as training set and remaining one third of the samples as the testing set. This kind of training is called generalization training [8]. Fig.…”

A comparison of techniques for developing predictive models of software metrics

“…Even though a large number of different neural network architectures and training algorithms exist, almost all published studies involving software metric models have been limited to this type [26][27][28][29][30][31][32][33][34][35] . This can be seen as a reflection of the lack of understanding of neural network techniques by many software metric researchers which is understandable given the tremendous growth in the neural network field in the past decade.…”