Machine Learning Approach for Software Reliability Growth Modeling with Infinite Testing Effort Function

Box 1

ANN feed-forward back-propagation procedure.

Step 1

  () Normalize the input and output data set patterns.

  () Initialize the weight values to small random numbers.

  () Set the error rate condition criteria.

  () Derive the activation functions for hidden layer and output layer from the mean value function.

Step 2

Calculate the Input value and Output value for hidden and output neurons using activation functions.

  () Output of Input Neuron = The input data value

  () The output of Hidden neurons or output neuron where NET = , is the activation function,

     is the weight from to and is the input data set pattern values.

Step 3

Calculate error , Start from output layer & work backward to hidden layers recursively.

     Error where is the error for output layer neurons.

Similarly calculate error for hidden layer neurons.

Step 4

Do the Weight Adjustments for output and hidden layers.

     Weight adjustments for output layer where here is the learning rate co-efficient

and the weights are adjusted by gradient descent method in which the weight change is proportional to the partial derivative

of the error.

     Repeat step to step until the stopping criteria are met.