A Study on Machine Learning Techniques Based Software Reliability Assessment

Software Reliability (SR) is a substantial aspect of software quality and one of the most important variables to consider when evaluating the quality of a software product. Software managers have a difficult task in selecting the optimum SR model for a real software development project. Throughout the software life cycle, it is concerned with the creation and maintenance of dependable software systems. Software measurement, software modeling, and software enhancement are 3 steps of a three-step method for boosting software dependability. Each of these stages is critical to establishing a solid software system. To control software quality, it is critical to improving accuracy in predicting dependability. A variety of measures for assessing software dependability have been suggested in the literature. Machine learning (ML) methods have been demonstrated to be effective in assessing many aspects of software dependability. To capture the many aspects of a software system, numerous machine learning approaches have been developed in this work. In this review paper the results of the various algorithms are compared. ML classifiers are compared with each other based on accuracy, precision, and recall parameters to determine the best classifier. Machine learning methods have been used in comparative analysis to estimate the defect level of the software instances. Comparison was placed among SVM, NB, DT, RF, and ANN classifiers. Where ANN shows the best results among other classifiers it has highest accuracy of 65.5%.