AN EFFECTIVE EARLY SOFTWARE RELIABILITY PREDICTION PROCEDURE FOR PROCESS ORIENTED DEVELOPMENT AT PROTOTYPE LEVEL EMPLOYING ARTIFICIAL NEURAL NETWORKS
K. Krishna Mohan, Ajit Kumar Verma, A. Srividya · International Journal of Reliability Quality and Safety Engineering · 2011
Reliability of a software product should be tracked during the software lifecycle right from the architectural phase to its operational phase. Heterogeneous systems consist of several globally distributed components, thus rendering their reliability evaluation more complex with respect to the conventional methods. In this context, reliability prediction of software process oriented systems assumes prime importance. It is important to take into account the proven processes like Rational Unified Process (RUP) to mitigate risks and increase the reliability of systems while building distributed based applications. This paper presents an algorithm using feed-forward neural network for early qualitative software reliability prediction. The inputs for neural networks consist of techno-complexity, practitioner's level, creation effort, size and leakage defects. The number of defects detected in each cycle can be predicted by using Artificial Neural Networks (ANN). Illustrative examples prove the effectiveness of the methodology employed.