Predicting Program Exit Code with LLMs and Programming Language Semantics
Quick summary
arXiv:2609.00579v1 Announce Type: cross Abstract: Large language models (LLMs) have shown proficiency in various software engineering tasks, such as code generation and translation. However, a key limitation in their performance may be their (lack of) understanding of programming-language semantics. Even when explicit semantics are given, it remains unclear whether LLMs apply those rules or lean on priors learned during pre-training instead. We study if LLMs lean on priors or given semantics with a novel task--Program Executability Prediction (PrEx)--that asks models to predict whether a progr
Key takeaways
- arXiv:2609.00579v1 Announce Type: cross Abstract: Large language models (LLMs) have shown proficiency in various software engineering tasks, such as code generation and translation.
- However, a key limitation in their performance may be their (lack of) understanding of programming-language semantics.
- Even when explicit semantics are given, it remains unclear whether LLMs apply those rules or lean on priors learned during pre-training instead.
Why it matters
“Predicting Program Exit Code with LLMs and Programming Language Semantics” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.
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