Master thesis: AI-Generated Testing for Case Management Systems: From Automated Test Design to Autonomous Test Execution

This thesis sits at the intersection of two rapidly evolving fields: AI and software quality assurance. The work has the potential

to directly influence how future enterprise systems are tested, enabling faster releases, improved quality, and more intelligent

automation. The results may also contribute to future AI-driven capabilities within iipax and other business-critical platforms.

Quality assurance is a critical part of software development, particularly for complex enterprise platforms such as iipax that

support business-critical case management processes in the public sector. Traditional testing approaches often rely on manually

created test cases, extensive regression testing, and significant effort to simulate realistic user behavior. As systems grow in

complexity and release frequency increases, these approaches become increasingly difficult to scale.

Recent advancements in Generative AI and AI Agents offer new possibilities for automating both the creation and execution of

software tests. By analyzing requirements, application configurations, process definitions, and existing test repositories, AI has

the potential to generate relevant test scenarios with minimal human intervention. Furthermore, AI agents may be able to execute

tests autonomously by simulating realistic end-user interactions, identifying defects, and evaluating system behaviour under

varying workloads.

The objective of this thesis is to investigate how AI can be leveraged to improve testing efficiency and quality within modern

case management platforms. The study will explore methods for AI-generated test case creation, automated regression testing, user

behaviour simulation, and AI-driven performance testing. The work includes evaluating available technologies, developing a

proof-of-concept solution, and measuring the effectiveness of AI-generated tests compared to traditional approaches.

The outcome of this thesis will provide both theoretical insights into AI-assisted software testing and practical recommendations

for how enterprise platforms such as iipax can leverage AI to improve quality assurance, reduce testing effort, and accelerate

delivery cycles.

• How effectively can Generative AI create relevant and maintainable test cases from existing test documentation, requirements,

and system configurations?

• Can AI agents autonomously execute functional and regression tests with comparable or improved defect detection compared to

traditional manual testing approaches?

We are looking for a curious and driven student with an interest in software development, artificial intelligence, and automation.

You enjoy solving complex problems, exploring new technologies, and turning ideas into practical solutions.

You are currently pursuing a university degree in Computer Science, Software Engineering, Information Technology, Artificial

Intelligence, or a related field.

You are a Swedish citizen (this is a requirement from several of our customers) and communicate fluently in both Swedish and

English.

Marie Pilsmo

marie.pilsmo@idainfront.se