We present an empirical evaluation of Large Language Models in code understanding associated with non-trivial, semantic-preserving program transformations such as copy propagation or constant folding. Our findings show that LLMs fail to judge semantic equivalence in approximately 41\% of cases when no context is provided and in 29\% when given a simple generic context. To improve accuracy, we advocate integrating LLMs with code-optimization tools to enhance training and facilitate more robust program understanding.
@article{arxiv.2504.00065,
title = {Assessing Code Understanding in LLMs},
author = {Cosimo Laneve and Alvise Spanò and Dalila Ressi and Sabina Rossi and Michele Bugliesi},
journal= {arXiv preprint arXiv:2504.00065},
year = {2025}
}