Choose one track. Master’s proposals target Spring 2027. Final scope and programme approval are agreed with the supervisor; bachelor scopes are suggested adaptations.
TRACK 01 · IMPLEMENTATION INCLUDED
Technical track
Develop a working solution and test whether it addresses the problem.
Possible research question
Can a traceability-aware tool help teams detect when AI-generated code or tests fail to satisfy their business requirements?
Suggested tasks
Read research on requirements traceability in AI-assisted development and compare existing solutions.
Identify one problem faced by developers and business stakeholders.
Write a research question and define what the solution should do.
Choose a small set of software requirements and define how each one can be checked.
Build a tool that links requirements to code changes and tests. Let an AI assistant suggest links for a person to review.
Compare it with ordinary issue tracking. Check whether reviewers identify missing requirements and incorrect links.
Explain what worked, what did not, and how the results compare with earlier research.
Evaluation, degree scope and deliverables
Study and evaluation
Compare the implemented solution with ordinary issue tracking and an AI assistant without explicit traceability. Combine reproducible technical tests with an appropriate empirical evaluation.
Missed requirements and incorrect traceability links
Verification time
Functional correctness and shared understanding
Degree scope
Bachelor’s
Build a requirements-to-test traceability view for one repository. Support manual links and basic suggestions; evaluate link correctness and review tasks.
Master’s
Test whether inspectable AI-suggested links improve verification and communication across technical and nontechnical roles.
Background
Programming and Git
Software testing
Basic requirements engineering
Possible deliverables
A focused literature review, justified problem and research question
A working prototype with source code and setup instructions
A reproducible comparison and an appropriate study of use
A report explaining design lessons, results and limitations
TRACK 02 · NO IMPLEMENTATION REQUIRED
Non-technical track
Study existing systems, information or work practices. You do not need to develop software.
Possible research question
How do teams maintain a shared understanding of requirements when AI participates in software development?
Suggested tasks
Read earlier studies of requirements traceability in AI-assisted development.
Choose one problem and write a research question the study can answer.
Interview developers and business stakeholders about ambiguity and verification responsibilities.
Trace selected requirements through existing issues, changes and tests in an authorised repository or prepared case.
Compare how different roles judge whether a delivered change satisfies the original need.
Analyse the interviews, observations or documents using a clearly described method. Look for disagreements as well as common patterns.
Explain the findings, compare them with earlier research and suggest practical improvements.
Evaluation, degree scope and deliverables
Study and evaluation
Use a bounded empirical study of requirements traceability in AI-assisted development. Justify case selection, recruitment and the analysis method. Distinguish observed behaviour from participants’ perceptions; use triangulation or a comparison where it serves the research question.
Loss of context and gaps in traceability
Shared understanding and allocation of verification work
Evidence for the findings, conflicting cases and limits of the study
Degree scope
Bachelor’s
Study one case or a small set of existing materials. Agree the interviews, documents or scenario tasks with the supervisor. Describe the method, analyse the findings and give practical recommendations.
Master’s
Use a clear research question and relevant IS theory. Justify the cases, participants and analysis method. Explain what the findings add to earlier research and where they may apply. No software development is required.
Background
Literature review and academic writing
Qualitative or quantitative research methods
Interest in requirements traceability in AI-assisted development; no programming prerequisite
Possible deliverables
A literature review and research question
A study plan and approved research material
An analysis supported by interviews, observations, documents or scenario results
A thesis with findings, recommendations and limitations
Scope and access
One repository and one requirements format. Use independently prepared acceptance tests, not only tests generated by the assistant. These implementation-related limits apply when developing or testing a technical solution. For a non-technical study, agree access to participants or existing materials early, use approved or fictional cases where appropriate, and distinguish perceptions from observed outcomes.
Agree access to data, participants or existing materials and any required ethics or privacy review before committing. A non-technical track needs a systematic study, not a working prototype.
Full academic proposal
Working topic
From Business Requirements to Verifiable AI-Assisted Software
Brief outline
This proposal examines requirements traceability in AI-assisted development in the work and information needs of developers and business stakeholders. The technical track combines a literature review and justified gap with requirements, design, implementation and evaluation of a bounded solution. The non-technical track investigates practices, experiences or organisational conditions through a systematic study of existing systems, documents or scenarios, without requiring implementation. Choose one track and agree the final research question, degree scope and contribution with the supervisor.
Programme fit
Information Systems. These are suggested research approaches, not a statement of confirmed programme policy. Agree the final title, track, degree scope and contribution with the supervisor and programme.
Shared research foundation
Review the literature; identify and justify a gap; formulate research questions; conduct a systematic study; analyse the evidence; explain the contribution relative to prior research and discuss limitations. The technical track additionally includes requirements, design, implementation and evaluation of an artifact.
