Do LLMs Truly Grasp Software Semantics? The Answer is Unclear

In a world where automation is rapidly infiltrating every nook and cranny of software development, large language models (LLMs) like Gemini-3, GPT-5.4, and Qwen-3.6 are making their presence felt. But can we trust these models to truly understand the complexities of software semantics, or are we setting ourselves up for a fall?

Lackluster Performance in Key Areas

These LLMs were put through their paces on 129 semantic tasks related to High-Level Message Sequence Charts (HMSCs). The results? A tepid 52% overall accuracy. While this might sound like a respectable figure at a glance, it's hardly confidence-inspiring when you consider the stakes involved. After all, HMSCs aren't just any visual models. they carry rigorous formal semantics and serve as a foundation for important tools like the Sequence Diagrams in the Unified Modelling Language (UML).

Breaking it down further, LLMs seem to have a decent grasp of basic semantic concepts, scoring around 88% accuracy. However, more complex tasks like semantic reasoning involving abstraction and composition, their performance plummets to 36%. The story doesn't improve much for traces and labelled transition systems (LTSs), where the accuracy sits at a discouraging 42%.

The Achilles' Heel: Semantic Reasoning

One might wonder why these sophisticated models struggle with semantic reasoning. What they're not telling you is that understanding co-regions and explicit causal dependencies in HMSCs is no small feat. None of the LLMs managed to employ these notions accurately in semantic-preserving transformations. Clearly, there's a gap between the models' touted capabilities and their actual performance.

Color me skeptical, but the enthusiasm for deploying LLMs across all stages of software development seems premature at best. While their abilities to handle basic queries and concepts are commendable, the heavy lifting of semantic reasoning remains largely out of reach. If we're serious about integrating AI into the software development lifecycle, this discrepancy needs more than just cursory attention.

What's Next for LLMs and Software Development?

So, what's the game plan here? Should we continue to rely on LLMs despite their limitations, or is it time to reassess our expectations? the potential for these models is enormous, but the current reality is less than stellar. Until they can bridge the gap between understanding simple constructs and performing complex semantic tasks, the promise of a fully automated software development process remains unrealized.

I've seen this pattern before: grand claims followed by underwhelming results. It's essential to remember that while LLMs can handle certain tasks with ease, their capacity for nuanced understanding is still a work in progress. For now, software developers would do well to proceed with caution and curiosity, but not blind faith.