QED at Large: A Survey of Engineering of Formally Verified Software
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Abstract (arXiv)
Development of formal proofs of correctness of programs can increase actual and perceived reliability and facilitate better understanding of program specifications and their underlying assumptions. Tools supporting such development have been available for over 40 years, but have only recently seen wide practical use. Projects based on construction of machine-checked formal proofs are now reaching an unprecedented scale, comparable to large software projects, which leads to new challenges in proof development and maintenance. Despite its increasing importance, the field of proof engineering is seldom considered in its own right; related theories, techniques, and tools span many fields and venues. This survey of the literature presents a holistic understanding of proof engineering for program correctness, covering impact in practice, foundations, proof automation, proof organization, and practical proof development.
Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Mar 2020.
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Citing papers per year: 2022: 1; 2023: 3; 2024: 4; 2025: 3; 2026: 2.
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- 2023LeanDojo: Theorem Proving with Retrieval-Augmented Language Modelscited by 427
- 2023Baldur: Whole-Proof Generation and Repair with Large Language Modelscited by 119
- 2024Formal Mathematical Reasoning: A New Frontier in AIcited by 81
- 2023LLMSTEP: LLM proofstep suggestions in Leancited by 32
- 2024miniCodeProps: a Minimal Benchmark for Proving Code Propertiescited by 18
- 2024CoqPilot, a plugin for LLM-based generation of proofscited by 11
- 2025APE-Bench: Evaluating Automated Proof Engineering for Formal Math Librariescited by 7
- 2025Steering LLMs for Formal Theorem Provingcited by 4
- 2024QEDCartographer: Automating Formal Verification Using Reward-Free Reinforcement Learningcited by 3
- 2025A Case Study on the Effectiveness of LLMs in Verification with Proof Assistantscited by 3
- 2022Towards a Responsible AI Development Lifecycle: Lessons From Information Securitycited by 2
- 2026Neuro-Symbolic Proof Generation for Scaling Systems Software Verificationcited by 1
- 2026From Natural Language to Verified Code: Toward AI Assisted Problem-to-Code Generation with Dafny-Based Formal Verification24 Apr 2026
- 2026Neuro-Symbolic Proof Generation for Scaling Systems Software Verification20 Mar 2026
- 2025A Case Study on the Effectiveness of LLMs in Verification with Proof Assistants26 Aug 2025
- 2025APE-Bench: Evaluating Automated Proof Engineering for Formal Math Libraries27 Apr 2025
- 2025Steering LLMs for Formal Theorem Proving21 Feb 2025
- 2024Formal Mathematical Reasoning: A New Frontier in AI20 Dec 2024
- 2024CoqPilot, a plugin for LLM-based generation of proofs25 Oct 2024
- 2024QEDCartographer: Automating Formal Verification Using Reward-Free Reinforcement Learning17 Aug 2024
- 2024miniCodeProps: a Minimal Benchmark for Proving Code Properties16 Jun 2024
- 2023LLMSTEP: LLM proofstep suggestions in Lean27 Oct 2023
- 2023LeanDojo: Theorem Proving with Retrieval-Augmented Language Models27 Jun 2023
- 2023Baldur: Whole-Proof Generation and Repair with Large Language Models8 Mar 2023
- 2024Formal Mathematical Reasoning: A New Frontier in AIcited by 81
- 2024Formal Mathematical Reasoning: A New Frontier in AI20 Dec 2024
- 2023Baldur: Whole-Proof Generation and Repair with Large Language Modelscited by 119
- 2023LLMSTEP: LLM proofstep suggestions in Leancited by 32
- 2024miniCodeProps: a Minimal Benchmark for Proving Code Propertiescited by 18
- 2024CoqPilot, a plugin for LLM-based generation of proofscited by 11
- 2025APE-Bench: Evaluating Automated Proof Engineering for Formal Math Librariescited by 7
- 2025Steering LLMs for Formal Theorem Provingcited by 4
- 2025A Case Study on the Effectiveness of LLMs in Verification with Proof Assistantscited by 3
- 2022Towards a Responsible AI Development Lifecycle: Lessons From Information Securitycited by 2
- 2026Neuro-Symbolic Proof Generation for Scaling Systems Software Verificationcited by 1
- 2026From Natural Language to Verified Code: Toward AI Assisted Problem-to-Code Generation with Dafny-Based Formal Verificationcited by 0
- 2026From Natural Language to Verified Code: Toward AI Assisted Problem-to-Code Generation with Dafny-Based Formal Verification24 Apr 2026
- 2026Neuro-Symbolic Proof Generation for Scaling Systems Software Verification20 Mar 2026
- 2025A Case Study on the Effectiveness of LLMs in Verification with Proof Assistants26 Aug 2025
- 2025APE-Bench: Evaluating Automated Proof Engineering for Formal Math Libraries27 Apr 2025
- 2025Steering LLMs for Formal Theorem Proving21 Feb 2025
- 2024CoqPilot, a plugin for LLM-based generation of proofs25 Oct 2024
- 2024miniCodeProps: a Minimal Benchmark for Proving Code Properties16 Jun 2024
- 2023LLMSTEP: LLM proofstep suggestions in Lean27 Oct 2023
- 2023Baldur: Whole-Proof Generation and Repair with Large Language Models8 Mar 2023
- 2022Towards a Responsible AI Development Lifecycle: Lessons From Information Security6 Mar 2022