Large language models (LLMs) have shown impressive abilities in generating code, even tackling complex programming tasks. But a critical question lingers: do these models truly *understand* the code they manipulate, or are they simply mimicking patterns seen in their training data? A new research paper, "An Empirical Study on Capability of Large Language Models in Understanding Code Semantics," dives deep into this question, exploring LLMs' grasp of code semantics. Researchers devised a clever evaluation framework called EMPICA to systematically test this understanding. EMPICA works by subtly transforming code snippets in ways that either preserve or alter the original meaning and then observing how LLMs react to these changes across tasks like code summarization, method name prediction, and output prediction. The ideal "code-understanding" LLM should produce consistent outputs for semantically equivalent code, and different outputs when the meaning changes. The results revealed a nuanced picture. While LLMs demonstrated some robustness to semantic-preserving transformations, meaning they generated similar outputs for code with the same meaning, their sensitivity to meaning-altering changes was less consistent. Interestingly, the study found that LLMs are better at recognizing when code remains semantically the same after transformation than they are at recognizing when changes alter the meaning. This suggests that while LLMs can pick up on surface-level patterns and mimic coding styles, their ability to reason about code logic and understand its deeper implications is still developing. This research has crucial implications for the future of software engineering. While LLMs can undoubtedly boost productivity, it’s important to recognize their current limitations in code understanding. Over-reliance on LLMs without proper validation could lead to bugs or vulnerabilities. This work sets the stage for developing more sophisticated testing methods that probe the true semantic comprehension of LLMs, paving the way for more reliable and trustworthy AI-powered coding tools.