Muhammad A. A. Pirzada

Muhammad A. A. Pirzada, Weiqi Wang, Yiannis Charalambous et al.

Formal verification of large C programs is impeded by state-space explosion: Bounded Model Checking (BMC) tools must encode the entire state space up to the predetermined bound by unrolling all nested constructs. We present ConVer, a top-down compositional verification tool. Given a C program with a top-level assertion, ConVer decomposes verification top-down: it uses a large language model (LLM) to synthesise function contracts from the system property, then alternates system-level and function-level checks in a CEGAR-CEGIS loop, refining contracts whenever a check fails via SMART ICE learning. We evaluate ConVer on four benchmark suites of increasing difficulty and against other state-of-the-art (SOTA) tools. On the Frama-C benchmark of 45 simple C programs, ConVer achieves 82-96% verification success across three LLM backends, with 93-95% of converged programs requiring only a single CEGAR-CEGIS iteration. On the X.509 parser benchmark (6~programs) and LF2C-Simple suite (17 programs), ConVer achieves 33-50% and 82-88% success respectively. On the VerifyThis suite of 11 recursive and loop-intensive programs, the Pre-Abstraction strategy achieves 55-64% success. In addition, we present ESBMC-LF a preprocessor tool that converts LF models to C while preserving the properties of the LF files, enabling ConVer to verify them. We transpile the LF Verifier Benchmarks using ESBMC-LF to C; we denote those LF-Hard. We show that ConVer successfully verifies 67% of LF-Hard benchmarks overall.

Muhammad A. A. Pirzada, Julian Parsert, Weiqi Wang et al.

Loop invariant synthesis remains a central and pivotal bottleneck in formal software verification. Recent LLM-based Neuro-Symbolic tools have achieved impressive solve rates. However, these tools rely on proprietary, often expensive cloud APIs, which constitute a hurdle for privacy-sensitive industrial deployments where the source code cannot leave the organisation or where cost is a factor. We present VerIbmc, a neuro-symbolic pipeline that pairs symbolic invariant generation with locally deployable open-weight language models with the ESBMC verification tool. Our pipeline combines a deterministic symbolic invariant synthesis phase with an iterative LLM refinement loop driven by structured verifier feedback. In addition, we provide two types of pipelines that differ in their prompting strategy: Chain-of-Thought vs. Tree-of-Thought. We conduct an extensive experimental evaluation with five open-weight models (ranging from 7B to 120B parameters) across five benchmark families comprising of 520 problems (499 after excluding 21 with unavoidable overflow). Overall, the best single configuration (GPT-OSS-120B) solves 431 of 499 problems (86.4%). Additionally, on the four benchmark suites shared with the strongest cloud-API tools, VerIbmc is competitive running only on a single local machine. The evaluation shows symbolic invariant synthesis solves 75 problems without any LLM call and yields up to +35 additional problems for the weakest model. Importantly, all inference runs entirely on a single local machine using open-weight models -- no cloud API or proprietary model is required. Overall, we demonstrate that a neuro-symbolic approach based on LLMs can be used effectively for invariant synthesis in a privacy-preserving and energy-efficient manner, without having to resort to expensive proprietary frontier models locked behind APIs.