ECE Master of Science Thesis Defense By: Joshua Steakelum
When: Tuesday,
April 19, 2022
10:00 AM
-
12:00 PM
Where: > See description for location
Cost: Free
Description: Topic: Covariate Software Defect Detection Models to Explicitly Characterize Changepoints
Location: Lester W. Cory Conference Room, Science & Engineering Building (SENG), Room 213A
ZOOM Teleconference: https://umassd.zoom.us/j/92427947982
Meeting ID: 924 2794 7982
Passcode: 399192
Abstract:
Large-scale software exhibits periods of increased defect discovery when blocks of less thoroughly tested code are introduced into an existing codebase. For example, the mission systems schedule of the F-35 Joint Strike Fighter includes multiple overlapping software blocks associated with various capabilities. Software reliability researchers have proposed changepoint models to characterize periods of increased defect discovery, where attempts to identify the location of these changepoints after testing have been performed. This is counterintuitive as conscious decisions drive software changepoints and the models are difficult to employ in a predictive manner. To overcome this limitation, this paper proposes a covariate software defect detection model capable of explaining changepoints in terms of common software testing activities and metrics such as software size estimation, code coverage, and defect density. The proposed and past models are compared with respect to their predictive accuracy and computational efficiency. Our results indicate that the proposed approach enables accurate prediction of the time required to achieve a desired defect discovery intensity or mean time to failure despite the practical challenges posed by changepoints in software.
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, Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth; Dr. Gokhan Kul, Assistant Professor, Department of Computer & Information Science, UMASS Dartmouth
*For further information, please contact Dr. Lance Fiondella via email at lfiondella@umassd.edu.
Location: Lester W. Cory Conference Room, Science & Engineering Building (SENG), Room 213A
ZOOM Teleconference: https://umassd.zoom.us/j/92427947982
Meeting ID: 924 2794 7982
Passcode: 399192
Abstract:
Large-scale software exhibits periods of increased defect discovery when blocks of less thoroughly tested code are introduced into an existing codebase. For example, the mission systems schedule of the F-35 Joint Strike Fighter includes multiple overlapping software blocks associated with various capabilities. Software reliability researchers have proposed changepoint models to characterize periods of increased defect discovery, where attempts to identify the location of these changepoints after testing have been performed. This is counterintuitive as conscious decisions drive software changepoints and the models are difficult to employ in a predictive manner. To overcome this limitation, this paper proposes a covariate software defect detection model capable of explaining changepoints in terms of common software testing activities and metrics such as software size estimation, code coverage, and defect density. The proposed and past models are compared with respect to their predictive accuracy and computational efficiency. Our results indicate that the proposed approach enables accurate prediction of the time required to achieve a desired defect discovery intensity or mean time to failure despite the practical challenges posed by changepoints in software.
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, Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth; Dr. Gokhan Kul, Assistant Professor, Department of Computer & Information Science, UMASS Dartmouth
*For further information, please contact Dr. Lance Fiondella 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, College of Engineering, Electrical and Computer Engineering