Measuring Software Reliability: A Trend Using Machine Learning Techniques

It has become inevitable for every software developer to understand, to follow that how and why software fails, and to express reliability in quantitative terms. This has led to a proliferation of software reliability models to estimate and predict reliability. The basic approach is to model past failure data to predict future behavior. Most of the models have three major components: assumptions, factors and a mathematical function, usually high order exponential or logarithmic used to relate factors to reliability. Software reliability models are used to forecast the curve of failure rate by statistical evidence available during testing phase. They also can indicate about the extra time required to carry out the test procedure in order to meet the specifications and deliver desired functionality with minimum number of defects. Therefore there are challenges whether, autonomous or machine learning techniques like other predictive methods could be able to forecast the reliability measures for a specific software application. This chapter contemplates reliability issue through a generic Machine Learning paradigm while referring the most common aspects of Support Vector Machine scenario. Couples of customized simulation and experimental results have been presented to support the proposed reliability measures and strategies.