A Review on Machine Learning Techniques to Predict the Reliability in Software Products

Abstract

AbstractThe software quality is identified by the use of a tool, which is defined as Software Reliability (SR). In many applications such as nuclear power plants, home applications, space missions, personal computers and, so on, software plays an important role in human lives over the few decades. Nowadays, the interactions of inter-component of modular software systems are very difficult to analyze by traditional SR methods due to the fast growth of complex software applications. Therefore, the SR prediction provides meticulous and remarkable results by developing the applications of Machine Learning (ML) techniques. In the research paper, the discussion of various ML techniques to predict the SR are presented along with the evaluation of selected performance criteria. According to the selected performance criteria, the evaluation of ML techniques to predict the SR based on the different datasets collected from industrial software, the prediction of SR are carried out by several ML techniques such as Support Vector Machines (SVM), Reduced Error Pruning Tree (REPTree), Instance-Based Learning, Neuro-fuzzy, General Regression Neural Network (GRNN), Linear Regression, Bagging, Feed Forward Back Propagation Neural Network (FFBPNN), and Multi-Layer Perceptions (MLP) are presented. To enhance the performance of techniques, this study helps the researchers effectively by discovering new meaningful knowledge about SR prediction.KeywordsBaggingMachine learningNeuro-fuzzySoftware reliabilitySoftware qualitySupport vector machine