Master Thesis - Release health monitoring

Build an AI-Assisted Release Health Monitor for the DDU ECU

The opportunity

How can software teams see whether a vehicle ECU is ready for release when important information is spread across different tools and teams?

In this thesis, you will explore that challenge by designing and building a prototype release health monitor for the DDU ECU. You’ll bring together selected release, quality, safety, and cybersecurity information in a dashboard that helps teams understand readiness, spot gaps, and make better-informed decisions.

The prototype will support human decision-making; it will not make release decisions. You’ll work with relevant stakeholders to understand their needs, select useful indicators, and evaluate whether the solution provides a clear and trustworthy overview.

What you’ll work on

• Explore how DDU release, quality, safety, and cybersecurity information is created and managed.
• Identify the indicators that are most useful for understanding release readiness.
• Design a common information model for status, risks, blockers, actions, evidence, ownership, and data freshness.
• Build a prototype dashboard connected to selected, approved data sources.
• Create useful views for stakeholders such as product owners, system owners, safety and cybersecurity roles, and developers.
• Identify patterns or highlight anomalies in selected release data.
• Evaluate the prototype with representative users and recommend how it could be developed further.

What you’ll deliver

• A working prototype dashboard for DDU release health monitoring.
• An information model describing the selected indicators and their sources, owners, and update frequency.
• Dashboard views covering release readiness, quality, risks, blockers, actions, and safety and cybersecurity status.
• An evaluation of the prototype’s usability, information quality, traceability, and usefulness.
• Recommendations for future development, governance, security, and integration.

Who should apply

This thesis is suited to students pursuing a master’s degree in Computer Science, Software Engineering, or Computer Engineering. You should enjoy software development and working with data from different sources. Experience or interest in dashboards, data integration, AI, embedded systems, automotive software, safety, or cybersecurity would be valuable.

Contact persons and supervisors

Linda Sandberg, Unit manager, linda.sandberg@scania.com
Reyhaneh Ezatpanah, Thesis supervisor, reyhaneh.ezatpanah@scania.com

Application:

Your application must include a CV, personal letter and transcript of grades

A background check might be conducted for this position. We are conducting interviews continuously and may close the recruitment earlier than the date specified.