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

About the company

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.

Your tasks

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.

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.

Potential proof-of-concept implementations may include

• Automatic generation of test cases from existing Excel-based test repositories.

• AI-generated test suites based on application configuration, workflows, roles, and business processes.

• AI agents that simulate different user roles such as administrators, registrars, reviewers, and case handlers.

• Automated execution of functional and regression tests using AI-driven agents.

• AI-generated load and performance testing scenarios based on realistic usage patterns.

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.

Examples of Research Questions

• 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?

• How can AI be used to simulate realistic user behaviour and workload patterns for performance and scalability testing?

• What benefits and limitations arise when introducing AI-generated testing into a customer project solution?

• Which testing activities provide the highest return on investment when augmented by AI?

Methodology

• Literature review of Generative AI, AI Agents, and AI-assisted software testing.

• Analysis of current testing practices within enterprise case management platforms.

• Evaluation of available AI frameworks and technologies for test generation and autonomous test execution.

• Development of a proof-of-concept solution for AI-generated test cases and/or autonomous test execution.

• Experimental comparison between traditional testing approaches and AI-generated testing.

• Measurement of quality, coverage, execution speed, defect detection capability, and maintenance effort.

Requirements

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.

Experience in one or more of the following areas is beneficial:

• Software development

• Test automation

• Machine Learning or Generative AI

• Cloud-native technologies

• Python, Java, JavaScript, or similar programming languages

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

How to apply

We look forward hearing from you, and if you have any further questions, you are welcome to contact us through:

Marie Pilsmo marie.pilsmo@idainfront.se