Code quality is an important aspect of programming education, with duplicate code being a common issue. To help students learn to avoid code duplication, it is useful to provide them with actionable, specific feedback, not just a generic code duplication warning. In this paper, we introduce the concept of diagnosable code duplication, provide an overview of its various types, and propose a framework for automatic detection. We apply the framework to an introductory programming dataset to demonstrate its ability to provide specific feedback and reveal non-trivial differences in detected cases compared to simpler detectors.