Coding LLMs Struggle to Predict How Software Actually Runs

Researchers have identified a blind spot in how coding language models understand software: while they can often write correct code, they struggle to reason about how that code will actually execute.

The Gap in Software Understanding#

Current benchmarks for evaluating coding AI focus largely on control flow—whether code produces the right outputs. But software engineering demands deeper reasoning: understanding performance constraints, memory usage, and computational complexity. The researchers call this missing capability the "software world model"—the internal knowledge that lets engineers and AI agents predict how software will behave when run.

A Broader Test for Real-World Demands#

Rather than simply checking whether code works, the study introduces new evaluation dimensions tied to execution resources. Alongside traditional test outcomes and exception types, the framework measures:

• Peak memory consumption during execution
• Wall-clock runtime performance
• Profiler outputs at both method and line-level granularity

The team built their evaluation using SWE-bench Verified, a dataset grounded in real software engineering tasks, ensuring the assessment reflects practical challenges rather than synthetic academic problems.

What the Results Show#

Testing revealed that even state-of-the-art models perform modestly on these execution-focused predictions and show brittle behavior—meaning small changes can cause large performance swings. This pattern points to a fundamental limitation: the models grasp how to write code but lack genuine insight into how software runs. The disparity suggests that current training approaches leave a significant gap between code generation ability and true systems-level reasoning.