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Assessing Code Understanding in LLMs (2504.00065v1)

Published 31 Mar 2025 in cs.SE, cs.AI, and cs.PL

Abstract: We present an empirical evaluation of LLMs 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.

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