In recent years, computer science education has increasingly focused on the development and application of automated code assessment methods. Test-based assessments of student submissions generate large quantities of data that could be used for learning analytics. Existing literature highlights a predominant use of unit tests for grading rather than for extracting insights into students’ challenges in learning programming concepts. Moreover, competence models, which systematically outline required learning outcomes, are increasingly important during curriculum design. We introduce a novel approach for systematically developing programming assignments aligned with competence models. This method also aids educators in creating unit tests that support competence-based learning analytics. As our method maps competences to individual test cases and thereby quantifies a student’s proficiency in an assignment, this helps educators to evaluate, whether students have the ability to understand and master the required competences. Experimental application of our method demonstrated enhanced clarity in understanding student assignments and a considerable improvement in the quality of learning analytics data.