Master Thesis Student

Intelligent Test Automation: Empirical and Architectural Evaluation of GitHub Copilot in Software Quality Assurance Workflows

Background

Modern software development relies heavily on automated testing to maintain speed, reliability, and quality across continuous integration and delivery (CI/CD) pipelines. As systems grow more complex and teams work in increasingly distributed environments, test automation becomes both essential and resource-intensive.

Designing, implementing, and maintaining high-quality test suites requires deep domain knowledge, technical expertise, and considerable manual effort. The recent emergence of AI-assisted coding tools, such as GitHub Copilot, has introduced a new paradigm for accelerating software development through intelligent code suggestion and natural language–driven programming assistance.

Goal of the thesis

The thesis will explore how GitHub Copilot can be effectively used to support and enhance test automation processes within the company's software quality assurance (SQA) framework. The students will perform controlled experiments comparing manually written and Copilot-generated tests across different levels—unit, API, and UI testing. Quantitative metrics such as development time, code coverage, mutation score, and defect detection rate will be collected and analyzed. The results will help assess whether Copilot improves productivity without sacrificing test quality or maintainability.

In addition to the empirical analysis, the thesis will also focus on architectural integration and human factors. The students will design a conceptual model or prototype showing how AI-assisted test generation can be embedded into CI/CD pipelines, enabling continuous feedback and automated validation of Copilot outputs. Interviews and surveys with QA engineers will complement the quantitative data, capturing perspectives on usability, trust, and adoption. The combined findings will lead to actionable recommendations for safely and effectively adopting AI tools in test automation.

About you

This thesis opportunity is ideal for
Master's students in Computer Science, Software Engineering, or related programs
who are
curious, motivated, and passionate
about the intersection of artificial intelligence and software quality.

Applicants should have a basic understanding of programming and software testing concepts, and a genuine interest in learning more about test automation, CI/CD workflows, and AI-assisted development. Enthusiasm for experimentation, problem-solving, and exploring new technologies will be more valuable than deep prior experience.

Number of students

2

Thesis Level

Master

Language

Thesis is to be written in English

Starting date

January 2026

Location

Kollmorgen Automation Office in Mölndal, Sweden

Additional information

We will provide computers and you will get great support from your supervisor and other colleagues

All students are compensated after the thesis is submitted and the final work meets the quality standard that is generally accepted by the university and presented at Kollmorgen.

Send in your application by submitting a CV and/or information about your studies and a short sentence about why you find this project interesting. If you get selected, the next step will be to have discussions with us to finalize the idea
.
Selection takes place on an ongoing basis, welcome with your application

Please apply before 18th of November 2025.