Master Thesis: AI-Powered Autonomous Test Environment Orchestration

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About this opportunity:

We are looking for a motivated Master's student to investigate how AI agents and data-quality-aware orchestration can reduce the effort and increase the reliability of integration test environment preparation.

Modern orchestration systems depend on configuration data, infrastructure metadata, and environment definitions originating from multiple sources. Inconsistent or low-quality data can lead to orchestration failures, environment-provisioning issues, and increased operational overhead.

This thesis will explore how AI, orchestration, and data management can be combined to create more reliable and autonomous test-environment workflows. The work complements related efforts in AI-driven test automation and supports the LaaS and OAS ambitions.

What you will do:

Investigate how data quality affects integration test environment preparation and orchestration reliability.

Develop a data-quality validation framework for configuration data, infrastructure metadata, and environment definitions.

Explore AI-assisted generation of test environments from available requirements and source data.

Investigate schema-validation concepts for improving the consistency and reliability of environment data.

Develop automated environment-readiness checks before test execution.

Build a proof of concept integrated with relevant orchestration workflows.

Evaluate the solution in terms of data quality, provisioning reliability, automation effort, and operational overhead.

Document the findings and provide recommendations for future development and production integration.

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 systems, data management, and automation.

You are interested in artificial intelligence, orchestration, data quality, and 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 CI/CD, test automation, infrastructure automation, or orchestration platforms.

Familiarity with schemas, configuration management, metadata, or data-validation techniques.

Knowledge of large language models, AI agents, data-quality frameworks, or automated reasoning.

Experience with cloud environments, distributed systems, containers, or Kubernetes.