Department Talk "Algorithms and Tools for Software Reliability Engineering" by Lance Fondella
Abstract: The recent National Academies report “Reliability Growth: Enhancing Defense System Reliability” offers several recommendations, including use of reliability growth models to empower hardware and software reliability management teams that direct contractor design and test activities. While there are many software reliability models, there are relatively few tools to automatically apply these models. Moreover, these tools are over two decades old and are difficult or impossible to configure on modern operating systems without a virtual machine. To overcome this technology gap, we are developing an open source software reliability tool for the Naval Air Systems Command (NAVAIR), the Department of Defense (DoD), and broader software engineering community. A key challenge posed by such a project is the stability of the underlying model fitting algorithms, which must ensure that the parameter estimates of a model are indeed those that best fit the data. If such model fitting is not achieved users who lack knowledge of the underlying mathematics may inadvertently use inaccurate predictions. This is potentially dangerous if the model underestimates important measures such as the number of faults remaining or mean time to failure (MTTF). To improve the robustness of the model fitting process, we are developing expectation maximization (EM) and expectation conditional maximization (ECM) algorithms to compute the maximum likelihood estimates of nonhomogeneous Poisson process (NHPP) software reliability growth models (SRGM). This talk presents an implicit ECM algorithm for the Weibull NHPP SRGM. The implicit approach eliminates computationally intensive integration from the update rules of the ECM, achieving a speedup of between 200 and 400 times that of explicit ECM methods. The enhanced performance and stability of these algorithms will ultimately benefit the DoD and contractor communities that use the open source software reliability tool.
