Automated Structural Analysis and Change Detection for No-Code Automation Workflows

This project focuses on developing and evaluating automated methods for analyzing complex no-code automation workflows used in enterprise environments. Platforms such as Workfront Fusion and Make.com allow organizations to build powerful automations quickly, but as these workflows grow in size and importance they often become difficult to understand, maintain, and safely modify. Changes are frequently made without clear visibility into downstream impacts, which increases operational risk and makes long-term governance challenging. The project will investigate techniques for parsing automation configurations, identifying structural relationships within workflows, and detecting meaningful differences between versions over time. In addition, the project will explore ways to generate clear, human-readable summaries and visual explanations that help technical and non-technical stakeholders understand how automations function.

The expected benefit to the partner organization is improved clarity and control over their automation systems. By making the structure and behavior of complex workflows easier to understand, the organization can reduce the risk of errors when changes are introduced, support more informed technical decision-making, and improve collaboration between developers, administrators, and business stakeholders. These capabilities will also help the organization scale its use of automation more responsibly by supporting better documentation, auditing, and long-term maintainability. Overall, the project aims to transform opaque automation configurations into understandable, manageable systems that can evolve safely as organizational needs change.

Faculty Supervisor:

Leah Bidlake

Student:

Partner:

Tekmera Inc.

Discipline:

Computer science

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

University of New Brunswick

Program: