Jenna DiVincenzo

Hazel Torek, Long Tien Nguyen, Priyam Gupta et al.

Unfolding expressions, which temporarily unfold a predicate to leverage its owned fields when evaluating a heap-dependent expression, and pure functions, which are heap-dependent functions that can be used in specifications, are used in deductive program verifiers based on implicit dynamic frames, such as Gradual C0, Gobra, Nagini, and SnaKt, to increase the modularity of specifications involving ownership. In this paper, we present the formal semantics for unfolding expressions and pure functions for a static verifier using symbolic execution, extend it for a gradual verifier, and provide a proof of soundness. To support Gradual C0, our proof is in the setting of gradual verification, a deductive program verification system that combines static and dynamic verification to allow partial specifications. However, because the gradual verifier is a conservative extension of a static verifier, our results also apply to static verifiers that use symbolic execution, such as the Silicon symbolic execution backend for the Viper verification infrastructure used by Gobra, Nagini, and SnaKt.

Wen Fan, Minh Tran, Sanya Dod et al.

Static verification tools can assure industrial scale software, but require significant human labor to write specifications. This is particularly true of static verifiers based on separation logic (SL verifiers), which excel at verifying heapmanipulating programs, but require many complex auxiliary specifications to reason about heap structure. Recent work applies large language models (LLMs) to generate code, tests, and proofs, including specifications for verifiers, but mostly targeting non-SL verifiers. To address this gap, this paper thoroughly evaluates how well LLMs perform when prompted to generate specifications for verifying 303 C functions with the SL verifier VeriFast. We explored eight prompting approaches, ten LLMs, and three input types in two stages. Quantitative and qualitative analyses are used to assess the LLM-generated code and specifications for functional behavior, verifiability and errors. The results show that LLMs preserve functional behavior in source code and specifications (both over 91%), but achieve modest verification success (31.4%). Using Gemini 2.5 Pro and providing formal contracts lead to higher success rates in our setting. Moreover, most errors (94%) come from LLMs' mistakes in the domainspecific knowledge of SL verifiers such as VeriFast. These findings provide guidance for optimizing LLM-generated specifications for SL verifiers.

Wen Fan, Marilyn Rego, Xin Hu et al.

Static verification is a powerful method for enhancing software quality, but it demands significant human labor and resources. This is particularly true of static verifiers that reason about heap manipulating programs using an ownership logic. LLMs have shown promise in a number of software engineering activities, including code generation, test generation, proof generation for theorem provers, and specification generation for static verifiers. However, prior work has not explored how well LLMs can perform specification generation for specifications based in an ownership logic, such as separation logic. To address this gap, this paper explores OpenAI's GPT-4o model's effectiveness in generating specifications on C programs that are verifiable with VeriFast, a separation logic based static verifier. Our experiment employs three different types of user inputs as well as basic and Chain-of-Thought (CoT) prompting to assess GPT's capabilities. Our results indicate that the specifications generated by GPT-4o preserve functional behavior, but struggle to be verifiable. When the specifications are verifiable they contain redundancies. Future directions are discussed to improve the performance.