When developing automatic quizzing systems and intelligent tutoring systems, significant effort has to be spent on developing the problem bank. Question and problem generation is a field of study concerning automating this routine work. The two most common methods used for generating complex problems are constrained random generation and template-based generation, but each of them has disadvantages. In this work, we study a possibility of generating code-tracing learning problems from existing open-source code. We generated a set of learning problems and evaluated their distinctness from human-authored problems and readiness for usage in the learning process. Both teachers and students showed the rate of determining machine-generated problems only slightly above random guessing. Teachers strongly agreed with the problems’ relevance and agreed with their suitability for the learning process. Automatic labeling to filter desired problems for a particular assignment includes used concepts, possible errors during solving, and difficulty estimates. The studied type of learning problem required little additional data to add to the code; our further work will concern problem types with more dynamic data to overcome this limitation.