aiqbench analysis
This benchmark establishes a rigorous evaluation framework using real-world Python property-based tests converted into Lean 4 specifications to assess AI agents in formal verification tasks. By leveraging a three-agent LLM pipeline, the study addresses the critical challenge of translating imperative code semantics into dependently-typed logic for automated proof generation. The release of open-source data and tools provides essential baselines for advancing AI-assisted software reliability as machine-generated code expands globally.
FVSpec: Real-World Property-Based Tests as Lean Challenges
arXiv:2606.01008v2 Announce Type: replace-cross Abstract: We present a benchmark for evaluating AI models and agents on real-world formal software verification tasks. We first scrape 11,039 property-based tests (PBTs) from real-world Python repositories, then automatically translate 2,772 of them (25%) into 9,415 Lean 4 specifications with sorry placeholders (about 3 formalizations/PBT; we retain multiple attempts when none dominates on quality metrics). Translating PBTs into Lean specifications is challenging: it requires modeling Python semantics in Lean, inferring the logical property encoded in an imperative PBT, and handling the inherent difficulties of dependently-typed programming in a seldom-used language. We describe a three-agent LLM pipeline for transpiling PBTs into Lean specifications, evaluate coverage and quality metrics, and provide baselines for proof generation using several automated and model based approaches. All code (scraper and agents) and data (PBTs and Lean specifications) are open source. Our benchmark aims to drive progress on the underexplored problem of AI-assisted formal verification of real-world software, which is of increasing interest as AI produces more and more of the world's code.
Abstract:We present a benchmark for evaluating AI models and agents on real-world formal software verification tasks. We first scrape 11,039 property-based tests (PBTs) from real-world Python repositories, then automatically translate 2,772 of them (25%) into 9,415 Lean 4 specifications with sorry placeholders (about 3 formalizations/PBT; we retain multiple attempts when none dominates on quality metrics). Translating PBTs into Lean specifications is challenging: it requires modeling Python semantics in Lean, inferring the logical property encoded in an imperative PBT, and handling the inherent difficulties of dependently-typed programming in a seldom-used language. We describe a three-agent LLM pipeline for transpiling PBTs into Lean specifications, evaluate coverage and quality metrics, and provide baselines for proof generation using several automated and model based approaches. All code (scraper and agents) and data (PBTs and Lean specifications) are open source. Our benchmark aims to drive progress on the underexplored problem of AI-assisted formal verification of real-world software, which is of increasing interest as AI produces more and more of the world's code.
Software Engineering (cs.SE); Artificial Intelligence (cs.AI)
arXiv:2606.01008 [cs.SE]
(or arXiv:2606.01008v2 [cs.SE] for this version)
https://doi.org/10.48550/arXiv.2606.01008 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 261 words
