Selene: Pioneering Automated Proof in Software Verification
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Abstract (arXiv)
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.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Jan 2024.
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Citing papers per year: 2024: 3; 2025: 8; 2026: 1.
12 papers.
- 2024Formal Mathematical Reasoning: A New Frontier in AIcited by 80
- 2024A Survey on Deep Learning for Theorem Provingcited by 55
- 2024Automated Proof Generation for Rust Code via Self-Evolutioncited by 31
- 2025VERINA: Benchmarking Verifiable Code Generationcited by 30
- 2025A Contemporary Survey of Large Language Model Assisted Program Analysiscited by 10
- 2025Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Taskscited by 9
- 2025OSVBench: Benchmarking LLMs on Specification Generation Tasks for Operating System Verificationcited by 8
- 2025FormalML: A Benchmark for Evaluating Formal Subgoal Completion in Machine Learning Theorycited by 5
- 2025Towards Repository-Level Program Verification with Large Language Modelscited by 4
- 2025RAG-Verus: Repository-Level Program Verification with LLMs using Retrieval Augmented Generationcited by 3
- 2025A Case Study on the Effectiveness of LLMs in Verification with Proof Assistantscited by 3
- 2026Neuro-Symbolic Proof Generation for Scaling Systems Software Verificationcited by 1
- 2026Neuro-Symbolic Proof Generation for Scaling Systems Software Verification20 Mar 2026
- 2025FormalML: A Benchmark for Evaluating Formal Subgoal Completion in Machine Learning Theory26 Sep 2025
- 2025Towards Repository-Level Program Verification with Large Language Models31 Aug 2025
- 2025A Case Study on the Effectiveness of LLMs in Verification with Proof Assistants26 Aug 2025
- 2025VERINA: Benchmarking Verifiable Code Generation29 May 2025
- 2025Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks26 May 2025
- 2025OSVBench: Benchmarking LLMs on Specification Generation Tasks for Operating System Verification29 Apr 2025
- 2025RAG-Verus: Repository-Level Program Verification with LLMs using Retrieval Augmented Generation7 Feb 2025
- 2025A Contemporary Survey of Large Language Model Assisted Program Analysis5 Feb 2025
- 2024Formal Mathematical Reasoning: A New Frontier in AI20 Dec 2024
- 2024Automated Proof Generation for Rust Code via Self-Evolution21 Oct 2024
- 2024A Survey on Deep Learning for Theorem Proving15 Apr 2024