Software Reliability Assessment: Modeling and Algorithms

Abstract

Non-homogeneous Poisson process (NHPP) software reliability growth models (SRGM) enable quantitative assessment of the software testing process. Software reliability models ranging from simple to complex have been proposed to characterize failure data that results from a variety of testing factors as well as non-uniform expenditure of testing effort. In order to predict the reliability of software accurately, it is important to apply models that both characterize the observed failure data well and make accurate predictions of the future. Efficient and robust algorithms to quickly estimate the model parameters despite inaccuracy in the initial estimates are also highly desirable. Ultimately, emphasis should be placed on predictive accuracy over complexity to best serve users of the research. This work presents the results of the preliminary contributions of the proposal including: (i) a heterogeneous single changepoint framework considering different models before and after the changepoint and (ii) comparison of testing effort models with a simple model as well as a testing effort model fit with an ECM algorithm to emphasize the importance of model predictive accuracy over increased model complexity. The preliminary findings will be used to serve as the basis of the overall contributions of the dissertation.