University Events

Description: Topic: Autoregressive Model Incorporating Windowing (ARMIW) Technique for Long-Term Prediction of Software Defect Discovery

Location: Science & Engineering Building (SENG), Room 222

Zoom Conference Link: https://umassd.zoom.us/j/99402468161
Meeting ID: 994 0246 8161 Passcode: 995673

Abstract:
The non-homogeneous Poisson process (NHPP) is a commonly used method for developing software reliability growth models (SRGM). These models are utilized for several significant predictions, including the remaining number of faults, defect rate, time to next defect, and reliability. However, SRGMs cannot be applied for long-term prediction using a limited amount of data. To overcome this limitation, we propose an autoregressive model incorporating the windowing (ARMIW) technique for improved prediction of software defects. We illustrate the ARMIW by applying it to several software defect times datasets. We also illustrate the long-term predictive capability of the ARMIW by fitting the model over several fit-test ratios. Our results demonstrate that the proposed modeling technique estimates the model parameters with higher precision in comparison to the traditional autoregressive (AR) models as well as the NHPP SRGMs and improves the time-series prediction accuracy. Results suggest that the autoregressive model incorporating windows improves the prediction of the defect discovery process significantly and makes better long-term predictions even using a small amount of available data. The advantage of incorporating windows in auto-regression is that they provide a better model fit.

Advisor(s): Dr. Lance Fiondella, Associate Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth

Committee Members: Dr. Hong Liu, Commonwealth Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth; Dr. Gokham Kul, Assistant Professor, Department of Computer & Information Science, UMASS Dartmouth

NOTE: All ECE Graduate Students are ENCOURAGED to attend.
All interested parties are invited to attend. Open to the public.

*For further information, please contact Dr. Lance Fiondella via email at lfiondella@umassd.edu