ASQ Statistics, Reliability & Risk, & Software Joint Webinar: Software Reliability Engineering: Algorithms and Tools 2308
ASQ Statistics, Reliability & Risk, & Software Joint Webinar: Software Reliability Engineering: Algorithms and Tools
While there are many software reliability models, there are relatively few tools to automatically apply these models. Moreover, these tools are decades old and difficult or impossible to configure on modern operating systems, even with a virtual machine. To overcome this technology gap, we are developing an open source software reliability tool for the software and system 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 characterize 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 overestimates the mean time to failure (MTTF). To improve the robustness of the model fitting process, we have developed expectation conditional maximization (ECM) algorithms to compute the maximum likelihood estimates of nonhomogeneous Poisson process (NHPP) software reliability models. This talk will present an implicit ECM algorithm, which eliminates computationally intensive integration from the update rules of the ECM algorithm, thereby achieving a speedup of between 200 and 400 times that of explicit ECM algorithms. The enhanced performance and stability of these algorithms will ultimately benefit the software and system engineering communities that use the open source software reliability tool. An overview of the Software Failure and Reliability Assessment Tool (SFRAT) will also be provided.

This talk should be of interest to government employees and contractors as well as private companies who desire to quantitatively assess the reliability of software they produce or acquire.

Lance Fiondella is an Associate Professor in the Department of Electrical & Computer Engineering at the University of Massachusetts Dartmouth and the Founding Director of the University of Massachusetts Dartmouth Cybersecurity Center. He received his PhD (2012) in Computer Science & Engineering from the University of Connecticut. Dr. Fiondella has published over 125 peer-reviewed journal articles and conference papers, twelve of which have been recognized with awards, including five as first author and seven with his students. His research has been funded by the United States Department of Homeland Security, Army Research Laboratory, Naval Air Systems Command, Naval Sea Systems Command, National Aeronautics and Space Administration, and National Science Foundation, including a CAREER award.

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Date & Time
Tuesday November 10th, 2020 10:00am CST
End Date & Time
Tuesday November 10th, 2020 11:00am CST
Meeting, Webinar
Host Type
Event Sponsor Information
Joint Webinar by ASQ Statistics Division,  Reliability & Risk Division and Software Division
Category Public Calendar

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Date & Time: 11/10/2020 11:00:00 AM EST

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