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Predicting Program Exit Code with LLMs and Programming Language Semantics — arXiv:2609.00579

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.00579
License (as archived): CC0-1.0
Archived: 2026-10-17T06:33:31.688Z
SHA-256 of the archived text: 1b0d04578801f323bef10103edc5b0c7cfaf414a9d0b47f73a3bb5bfe12ee860
Stable URL of this page: https://metraipolis.com/documento/dc_ebec3ea38bb4

How to cite

Predicting Program Exit Code with LLMs and Programming Language Semantics — arXiv:2609.00579 — arXiv (en), https://arxiv.org/abs/2609.00579 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ebec3ea38bb4 (Metraipolis archive dc_ebec3ea38bb4, sha256 1b0d04578801f323bef10103edc5b0c7cfaf414a9d0b47f73a3bb5bfe12ee860)

BibTeX

@misc{dcebec3ea38bb4,
  title = {Predicting Program Exit Code with LLMs and Programming Language Semantics — arXiv:2609.00579},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.00579},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ebec3ea38bb4; sha256 1b0d04578801f323bef10103edc5b0c7cfaf414a9d0b47f73a3bb5bfe12ee860},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.00579. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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 program is semantically valid or invalid (and, if invalid, which formal rule it violates) given the program's syntax and operational semantics. Because PrEx requires both valid and invalid programs, we build a dataset with systematically generated invalid transformations derived from valid programs. We evaluate open-source coding LLMs under two semantic formalisms and two semantic shifts across Human-Written, LLM-Translated, and Fuzzer-Generated program splits. Our findings show that LLMs lean on pre-training priors rather than systematically applying the given rules, performing especially poorly on modified semantics and degrading further as program complexity increases. PrEx is available at this https URL .

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-02 in cs.AI).
Categorie arXiv: Programming Languages (cs.PL) ; Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Software Engineering (cs.SE).
Pagina del lavoro: https://arxiv.org/abs/2609.00579 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T06:15:12.804Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-02?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).