ReDefining Code Comprehension: Function Naming as a Mechanism for Evaluating Code Comprehension

"Explain in Plain English" (EiPE) questions are widely used to assess code comprehension skills but are challenging to grade automatically. Recent approaches like Code Generation Based Grading (CGBG) leverage large language models (LLMs) to generate code from student explanations and validate its equivalence to the original code using unit tests. However, this approach does not differentiate between high-level, purpose-focused responses and low-level, implementation-focused ones, limiting its effectiveness in assessing comprehension level. We propose a modified approach where students generate function names, emphasizing the function's purpose over implementation details. We evaluate this method in an introductory programming course and analyze it using Item Response Theory (IRT) to understand its effectiveness as exam items and its alignment with traditional EiPE grading standards. We also publish this work as an open source Python package for autograding EiPE questions, providing a scalable solution for adoption.