Pith. sign in

REVIEW 2 cited by

Selene: Pioneering Automated Proof in Software Verification

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2401.07663 v2 pith:IZ3FJ52G submitted 2024-01-15 cs.SE

classification cs.SE
keywords proofseleneverificationautomatedsoftwarecorrectnessgenerationllms
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Ensuring correctness is a pivotal aspect of software engineering. Among the various strategies available, software verification offers a definitive assurance of correctness. Nevertheless, writing verification proofs is resource-intensive and manpower-consuming, and there is a great need to automate this process. We introduce Selene in this paper, which is the first project-level automated proof benchmark constructed based on the real-world industrial-level operating system microkernel, seL4. Selene provides a comprehensive framework for end-to-end proof generation and a lightweight verification environment. Our experimental results with advanced large language models (LLMs), such as GPT-3.5-turbo and GPT-4, highlight the capabilities of LLMs in the domain of automated proof generation. Additionally, our further proposed augmentations indicate that the challenges presented by Selene can be mitigated in future research endeavors.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. RAG-Verus: Repository-Level Program Verification with LLMs using Retrieval Augmented Generation

    cs.SE 2025-02 conditional novelty 6.0 of 10

    Retrieval-augmented prompting improves LLM proof-completion pass rates by 27% relative on a new repository-level Verus benchmark and triples them on a function-level benchmark at low sampling budgets.

  2. A Contemporary Survey of Large Language Model Assisted Program Analysis

    cs.SE 2025-02 conditional novelty 1.0 of 10

    A review that catalogs how large language models are used in static, dynamic, and hybrid program analysis, and outlines open challenges.

Pith tools