Master Thesis: AI-Driven Test Failure Analysis and Self-Healing Automation

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About this opportunity:

We are looking for a motivated Master's student to investigate how AI agents can improve large-scale test automation by analysing failed test executions, determining probable root causes, and recommending or performing corrective actions.

In large-scale test automation systems, substantial engineering time is spent analysing failed test runs. The challenge is to understand failures quickly enough to maintain delivery velocity. Many failures are repetitive and may be caused by infrastructure issues, environment drift, configuration defects, known flaky tests, or dependency problems.

This thesis will explore how AI-enabled automation platforms can reduce manual failure analysis and improve test automation productivity. The work is connected to the RCE AI Transformation workstream and OAS ownership of test automation, and reflects current industry trends in intelligent software engineering.

What you will do:

- Investigate approaches for analysing failed test executions and identifying probable root causes.
- Develop a failure classification engine for recurring test failures.
- Design and evaluate AI-assisted root-cause analysis methods.
- Develop failure-clustering methods to identify related or repeated failures.
- Generate remediation recommendations based on failure patterns and available context.
- Optionally, develop a self-healing prototype for selected failure types.
- Define an evaluation methodology and test the proposed solution on relevant test data.
- Analyse limitations, risks, and opportunities for integrating AI into test automation workflows.
- Document the findings and present recommendations for future development.

The skills you bring:

- You are enrolled in a Master's programme in Computer Science, Software Engineering, Electrical Engineering, or a related field.
- You have programming experience, preferably in Python or a similar language.
- You have a basic understanding of software testing, test automation, and debugging.
- You are interested in artificial intelligence, machine learning, data analysis, or intelligent software systems.
- You have strong analytical and problem-solving skills.
- You have good technical writing and communication skills.

The following are considered a plus:

- Experience with test automation frameworks, CI/CD pipelines, or software quality engineering.
- Familiarity with log analysis, failure classification, clustering, or root-cause analysis.
- Knowledge of large language models, AI agents, retrieval-augmented generation, or anomaly detection.
- Experience with cloud environments, distributed systems, or infrastructure automation.