Software quality monitoring using AI/ML techniques

This project aims at employing AI and machine learning techniques to monitor and improve software quality. The quality of the system is to be measured by several metrics including the number of existing software defects, the normal/abnormal behavior of the system, test quality, test coverage, etc. The project focuses on studying historical data and trends of software defects with the goal of using these data to predict software quality. Machine learning approaches leveraged in this work would allow Ericsson to better monitor software quality of their systems, which would result in more effective and easier software maintenance. The interns involved in this project will be trained in building robust ML pipelines and solving industrial problems. By collaborating with the worldwide 5G equipment and services leader Ericsson, the interns will gain industrial experience and validate research advances on industrial data.

Faculty Supervisor:

Olga Baysal

Student:

Partner:

Ericsson Canada Inc (Ottawa, ON)

Discipline:

Computer science

Sector:

Information and cultural industries; Manufacturing; Professional, scientific and technical services

University:

Carleton University

Program: