University Events

Description: Topic: A STOCHASTIC PETRI NET MODEL OF CONTINUOUS INTEGRATION AND CONTINUOUS DELIVERY

Location: Lester Cory Conference Room (SENG), Room 213A

Zoom Conference Link: https://umassd.zoom.us/j/96731748662
Meeting ID: 967 3174 8662
Passcode: 782013

Abstract:
Modern software development organizations rely on continuous integration and continuous delivery (CI/CD), since it allows developers to continuously integrate their code in a single shared repository and automates the delivery process of the product to the user. While modern software practices improve the performance of the software life cycle, they also increase the complexity of this process. Past studies make improvements to the performance of the CI/CD pipeline. However, modern software development involves several interrelated factors that can affect performance and production efforts. Risk assessment is a critical factor in preserving the performance of the CI/CD pipeline. Recent research has been conducted on leveraging machine learning to enhance various aspects of the software engineering process. Despite significant progress, there is a lack of corresponding models to evaluate the implications of machine learning on the overall software development process Yet, there are fewer formal models to quantitatively guide process and product quality improvement or characterize how automated and human activities compose and interact asynchronously. Therefore, this thesis introduces models for evaluating CI/CD pipelines, with a particular emphasis on assessing the probability of successful product delivery across various stages, including the reliability of machine learning. By analyzing the impact of machine learning advancements, valuable insights are obtained to enhance performance, encompassing delivery time and potential product quality. The utility of the model is demonstrated through a sensitivity analysis to identify stages of the pipeline where improvements would most significantly improve the probability of timely product delivery. The model provides unbiased insights into resource allocation to optimize machine learning outcomes and achieve overarching objectives. It emphasizes the importance of a systematic approach to ensure the effective utilization of machine learning. Additionally, the model offers objective insights into the reduction of failure rates, deployment failures, and risk detection time through machine learning.

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

Committee Members:
Dr. Hong Liu, Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth
Dr. Gokhan 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