Master Thesis: Adaptive Test Selection using Knowledge Bases and Code Property Graphs
Based in Sweden
Core
Master thesis research on adaptive test selection using code property graphs (CPGs) and knowledge bases to optimize test suite execution for software changes.
Role type
Master Thesis Researcher (Software Engineering/AI)
Builds
A method and tooling for adaptive test case selection at the Application Component level.
Domain
Software Engineering, AI, Code Analysis
Deliverable
research
Required skills
AI, Computer Science, Programming
Responsibilities
Extract code property graphs (CPGs) to capture areas impacted by changes; Combine CPGs with knowledge bases (enterprise architecture models, component-artifact-test relationships); Evaluate the effectiveness of CPGs plus knowledge bases in identifying relevant tests; Build a method and tooling for test case selection.