Automation of task sequencing and allocation

The National Bank of Canada (BNC) is integrating artificial intelligence (AI) in their software development processes. Within their software projects, the team is continuously improving solutions and integrating them within the organization. A key issue in the development and integration cycle is allocating team resources to different tasks. Short-term development tasks (completed under “sprints” in the agile development terminology) can be assigned to different team members based on factors such as current workload, skillset, experience and interests. These factors are manually collected to optimize task sequencing and allocation.
The goal of this project is to automate the integration of such factors into the current project management toolset at BNC. The project will utilize machine learning techniques to learn from previously completed tasks and propose a decision support system that recommends the most suitable team members to be allocated for each specific task.

Faculty Supervisor:

Emad Shihab

Student:

Partner:

Banque Nationale du Canada

Discipline:

Computer science

Sector:

Finance and Insurance; Professional, scientific and technical services

University:

Concordia University

Program: