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Wednesday, May 25, 2016
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2:00 PM
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3:00 PM
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DMPTool
- Location: OFD Lounge, Library 220
- Contact: > See Description for contact information
- Description: SPA TEA Time: DMPTool Zak Painter presenting
Contact: Sponsored Programs Administration
- Topical Areas: Faculty, Staff and Administrators
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9:30 PM
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11:00 PM
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MASTER OF SCIENCE PROJECT DEFENSE BY: Karthik Raj Katipally
- Location: Science & Engineering Building, Lester W. Cory Conference Room: Room 213A
- Cost: free
- Contact: ECE: Electrical & Computer Engineering Department
- Description: TOPIC: DESIGN AND TEST OF AN OPEN-SOURCE SOFTWARE RELIABILITY TOOL
LOCATION: Lester W. Cory Conference Room, Science & Engineering Building (Group II), Room 213A
ABSTRACT:
This master's project is part of a software reliability research project to design and test an open-source software reliability tool. The goal of this project was to design a tool to assess the reliability of software from failure data. The tool enables large software vendors to track the reliability growth of their software and predict reliability after release. The tool implements mathematical software reliability growth models (SRGM). Tools implementing SRGM are available. However, the distinguishing factor of this tool is an open-source web-based tool, which can provide software reliability estimation as a service. This presentation describes a detailed procedure to integrate a new SRGM into the tool, referred to simply as the software reliability tool (SRT) and possesses a modular approach to integrate any new or existing model.
The model architecture is loosely coupled with the SRT architecture, simplifying integration to allow a contributor to focus on the mathematical details of their model rather than the design elements of the tool architecture. The tool architecture validates the model and integrates it transparently. Contributors can apply their model to failure data immediately after model validation. This flexible architecture will allow reliability researchers and practitioners to integrate their own models and use the tool under an open-source license agreement. The contributors can also compare models with respect to measures such as robustness of data handling and efficiency. Model validation requires that a contributor follow procedural steps and conventions essential for model integration. The project report also serves as a guide for model contributors and an overview of the necessary details of the tool architecture.
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 and Dr. Honggang Wang, Department of Electrical & Computer Engineering
*For further information, please contact Dr. Lance Fiondella at 508.999.8596, or via email at lfiondella@umassd.edu.
- Topical Areas: General Public, University Community, College of Engineering, Electrical and Computer Engineering
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Call for Nominations: Provost's Award
- Location: Online
- Contact: CITS Instructional Development
- Description: The Provost's Teaching & Learning with Technology award was established in 2007 to recognize excellence in teaching and learning with technology.
The 2016 award recipient will receive $2000, an award plaque, and their name will be displayed on the Provost’s Award plaque in CITS Instructional Development.
See link for further information about award eligibility and directions for submitting nominations.
Deadline: 5:00pm EDT, Tuesday, May 31, 2016.
- Link: http://instructionaldev.umassd.edu/provosts-award/
- Topical Areas: audience: Faculty, audience: Staff, topic: Faculty Development
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11:00 AM
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1:00 PM
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MASTER OF SCIENCE PROJECT DEFENSE BY: Varun Kumar Reddy Garlapati
- Location: Science & Engineering Building, Lester W. Cory Conference Room: Room 213A
- Cost: free
- Contact: ECE: Electrical & Computer Engineering Department
- Description: TOPIC: EXPECTATION CONDITIONAL MAXIMIZATION (ECM) ALGORITHMS FOR THE TRUNCATEDLOGISTIC AND TRUNCATED EXTREME VALUE MINIMUM SOFTWARERELIABILITY GROWTH MODELS
LOCATION: Lester W. Cory Conference Room, Science & Engineering Building (Group II), Room 213A
ABSTRACT:
Non-Homogeneous Poisson Process (NHPP)-based Software Reliability Growth Models (SRGM) can estimate different quantitative metrics such as the future number of failures and failure intensity. SRGM are complex parametric mathematical functions which are estimated from failure data with methods such as Maximum likelihood estimation (MLE) using different computational procedures such as Newton's Method. The Expectation Maximization (EM) algorithm enables MLE when the failure data is incomplete and possesses desirable properties such as monotonicity in convergence to the maximum likelihood estimate.
This project implemented Expectation Conditional Maximization (ECM) algorithms for the TLOGIS (Truncated Logistic Model) and TEVMIN (Truncated Extreme Value Minimum) SRGM. Initial parameter estimation techniques based on the EM algorithm were also developed. Convergence of the ECM was rapid, requiring 8-10 iterations. Robustness of the models to missing initial data was also considered. Deleting the initial 30 failures negatively impacted model prediction, but the models predicted future failures relatively well when 50 initial failures were removed.
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 and Dr. Honggang Wang, Department of Electrical & Computer Engineering
*For further information, please contact Dr. Lance Fiondella at 508.999.8596, or via email at lfiondella@umassd.edu.
- Topical Areas: General Public, University Community, College of Engineering, Electrical and Computer Engineering
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2:00 PM
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3:00 PM
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TEA Time w/ SPA: DMPTool
- Location: OFD Lounge, Library 220
- Contact: > See Description for contact information
- Description: TEA Time with SPA
Topic: DMPTools
Presenter: Zac Painter
For more information contact Sponsored Projects Administration
- Topical Areas: Research, Sponsored Projects Administration
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