MASTER OF SCIENCE THESIS DEFENSE BY: Vidhyashree Nagaraju
When: Wednesday,
December 9, 2015
4:00 PM
-
6:00 PM
Where: Science & Engineering Building, Lester W. Cory Conference Room: Room 213A
Cost: Free
Description: TOPIC: ALGORITHMS FOR NONHOMOGENEOUS POISSON PROCESS (NHPP)
SOFTWARE RELIABILITY MODELS AND SOFTWARE REJUVENATION
CONSIDERING CORRELATED FAILURES
LOCATION: Lester W. Cory Conference Room, Science & Engineering Building (Group II), Room 213A
ABSTRACT:
Nonhomogeneous Poisson process software reliability growth models (SRGM) enable several quantitative metrics that can be used to guide important decisions during the software engineering life cycle such as testing resource allocation and release planning. However, many of these SRGM possess complex mathematical forms that make them difficult to apply in practice. Specifically, traditional statistical procedures such as maximum likelihood estimation must solve a system of non-linear equations to identify the numerical parameters that best characterize a set of failure data. Recently, researchers have made significant progress toward overcoming this difficulty by developing an expectation-maximization (EM) algorithm that exhibits better convergence properties and can therefore find the maximum likelihood estimates of complex SRGM with greater ease. However, this approach is computationally intensive and therefore slow for complex models. This thesis presents: (i) adaptive EM and (ii) Implicit expectation conditional maximization (IECM) algorithms to avoid the computationally intensive steps present in previous techniques and to identify the MLEs with respect to all model parameters.
In software reliability models, another critical metric is availability. Phenomenon such as software aging can cause failure of a critical application which may result in a significant loss of life, compromise confidential data, or produce economic loss. Software aging is addressed by a method known as software rejuvenation, which is a proactive fault management technique with the goal of postponing or preventing crash failures and/or performance degradation. Most rejuvenation methods ignores the impact of correlation on the reliability of a component based system. Therefore, this thesis presents: (iii) an extended software rejuvenation model to explicitly consider correlated failures, which enables the computation of software rejuvenation schedules to achieve high availability and low downtime cost.
NOTE: All ECE Graduate Students are ENCOURAGED to attend.
All interested parties are invited to attend. Open to the public.
Advisor: Dr. Lance Fiondella
Committee Members: Dr. Liudong Xing, Department of Electrical & Computer Engineering and Dr. Panlop Zeephongsekul, School of Mathematical and Geospatial Sciences, RMIT University
*For further information, please contact Dr. Lance Fiondella at 508.999.8596, or via email at lfiondella@umassd.edu.
SOFTWARE RELIABILITY MODELS AND SOFTWARE REJUVENATION
CONSIDERING CORRELATED FAILURES
LOCATION: Lester W. Cory Conference Room, Science & Engineering Building (Group II), Room 213A
ABSTRACT:
Nonhomogeneous Poisson process software reliability growth models (SRGM) enable several quantitative metrics that can be used to guide important decisions during the software engineering life cycle such as testing resource allocation and release planning. However, many of these SRGM possess complex mathematical forms that make them difficult to apply in practice. Specifically, traditional statistical procedures such as maximum likelihood estimation must solve a system of non-linear equations to identify the numerical parameters that best characterize a set of failure data. Recently, researchers have made significant progress toward overcoming this difficulty by developing an expectation-maximization (EM) algorithm that exhibits better convergence properties and can therefore find the maximum likelihood estimates of complex SRGM with greater ease. However, this approach is computationally intensive and therefore slow for complex models. This thesis presents: (i) adaptive EM and (ii) Implicit expectation conditional maximization (IECM) algorithms to avoid the computationally intensive steps present in previous techniques and to identify the MLEs with respect to all model parameters.
In software reliability models, another critical metric is availability. Phenomenon such as software aging can cause failure of a critical application which may result in a significant loss of life, compromise confidential data, or produce economic loss. Software aging is addressed by a method known as software rejuvenation, which is a proactive fault management technique with the goal of postponing or preventing crash failures and/or performance degradation. Most rejuvenation methods ignores the impact of correlation on the reliability of a component based system. Therefore, this thesis presents: (iii) an extended software rejuvenation model to explicitly consider correlated failures, which enables the computation of software rejuvenation schedules to achieve high availability and low downtime cost.
NOTE: All ECE Graduate Students are ENCOURAGED to attend.
All interested parties are invited to attend. Open to the public.
Advisor: Dr. Lance Fiondella
Committee Members: Dr. Liudong Xing, Department of Electrical & Computer Engineering and Dr. Panlop Zeephongsekul, School of Mathematical and Geospatial Sciences, RMIT University
*For further information, please contact Dr. Lance Fiondella at 508.999.8596, or via email at lfiondella@umassd.edu.
Contact:
ECE: Electrical & Computer Engineering Department 508.999.9164 http://www.umassd.edu/engineering/ece/
Topical Areas: General Public, University Community, Electrical and Computer Engineering