Comparative Analysis of Software Reliability Prediction Using Machine Learning and Deep Learning
Akshat Jindal, Ashi Gupta, Rahul
Abstract
Software Reliability is an integral part to determine Software Quality. Software is considered to be of high quality if its reliability is high. There exist many statistical models that can help in predicting Software Reliability, but it is very difficult to consider all the real-world factors and hence it makes the task of reliability prediction very difficult. Therefore, it becomes more challenging for the IT industry to predict if a software is dependable or not. Machine Learning and Deep Learning can be used for the prediction of Software Reliability by programming a model that assesses reliability by fault prediction in a more meticulous manner. Therefore, in this study the use of predefined Artificial Intelligence algorithms, mainly Artificial Neural Network (ANN), Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) are intended for predicting software reliability on a time series software failure dataset and are compared on the basis of selected performance metrics. Each of the algorithm trained on software failure dataset will be used to predict the software failure time after a certain number of corrective modifications are performed on the software. Based on the result of the studies, it is discovered that LSTM produces superior outcomes in predicting the software failure trend as it can capture long and short-term trends in the software failure dataset.