A Survey on Software Reliability Assessment by Using Different Machine Learning Techniques

Software reliability is a special aspect of reliability engineering. System reliability, by definition, includes all parts of the system, including hardware, software, supporting infrastructure (including critical external interfaces), operators and procedures. Software reliability is a key part in software quality. The study of software reliability can be categorized into three parts: modeling, measurement and improvement. Software reliability modeling has matured to the point that meaningful results can be obtained by applying suitable models to the problem. There are many models exist, but no single model can capture a necessary amount of the software characteristics. Assumptions and abstractions must be made to simplify the problem. There is no single model that is universal to all the situations. Software reliability measurement is naive. In this paper, we propose various machine learning approaches or techniques for the assessment of software reliability such as fuzzy approach, neuro-fuzzy approach, artificial neural network approach, genetic algorithm approach, Bayesian classification approach, support vector machine (SVM) approach, Self-organizing map approach. Also, In this paper we investigate the performance of some of the well known machine learning techniques in predicting software reliability.