One of the quantifiable credits of software quality is reliability.Programmable/ Software Reliability Growth Model (SRGM) can be used for continuous quality during difficult times. In all conditions where test work fluctuates over time, the customary time-sensitive SRGM may not be clear enough. In order to close this gap, testing work was used instead of time in SRGM. It may be unwise to put forward a restricted test pressure limit in advance because the test work will be endless within the incomprehensible test time. Later in this article, we propose a permanent test stress service related to the old inhomogeneous Poisson process model (NHPP). We use an artificial neural network (ANN) to configure the proposed model, which contains frustration data from the software. Here, it is reasonable to obtain a huge load of game plans for the comparison model, which represents past disappointment data in a comparable way. We use artificial intelligence methods to select game plans with reasonable load for the model to describe the past and future data well. We use a reasonable software disappointment data set to decompose the presentation of the proposed model from the current model. Use the artificial neural network method to design the general Direct Software Reliability Growth Model (SRGM) through test work.: The true quality software is shown by current research mainly focuses on the best method of general guessing modeling.
Application of Artificial Neural Network for Software Reliability Growth Modeling with Testing Effort
