Search arXivSearch

arXiv · 2607.17622

BTOR2-Based C Program Verification via Hardware Model Checking

Abstract

Program verification tools often rely on specific intermediate representations and analysis backends, limiting the reuse of verification algorithms and model checkers across frameworks. In contrast, hardware model checking has developed a mature backend ecosystem, where standard formats such as BTOR2 support reusable algorithms for counterexample search and inductive safety proving. Applying these capabilities to C requires translating assertion-based programs into transition systems that hardware model checkers can directly process. We present C2Btor, a method for encoding such verification tasks into BTOR2 models. C2Btor uses a program counter to capture control transfers, represents data states and memory objects with bit-vectors and arrays, and maps assumptions and assertion checks into BTOR2 constraints and bad-state properties. We evaluate C2Btor on SV-COMP C ReachSafety benchmarks and a curated assertion-category benchmark suite, comparing it with representative program verification tools. C2Btor correctly solves 263 tasks, 101 more than CBMC configured with bounded model checking, and is especially effective on bit-vector benchmarks, where it solves 75.5% of the tasks with no wrong verdicts. These results show that the BTOR2 route allows C program verification to benefit from advances in hardware model-checking backends, expanding the available capability for counterexample search, inductive safety proving, and word-level transition-system reasoning.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Xinyu Zhang, Runxuan Fang, Ziqun Bao, Yechuan Xia, Jianwen Li, Geguang Pu. 2026-07-20. BTOR2-Based C Program Verification via Hardware Model Checking. https://arxiv.org/abs/2607.17622

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Combining Type Inference and Automated Unit Test Generation for Python

Automated unit test generation is an established research field that has so far focused on statically-typed programming languages. The lack of type information in dynamically-typed programming languages, such as Python, inhibits test generators, which heavily rely on information about parameter and return types of functions to select suitable arguments when constructing test cases. Since automated test generators inherently rely on frequent execution of candidate tests, we make use of these frequent executions to address this problem by introducing type tracing, which extracts type-related information during execution and gradually refines the available type information. We implement type tracing as an extension of the Pynguin test-generation framework for Python, allowing it (i) to infer parameter types by observing how parameters are used during runtime, (ii) to record the types of values that function calls return, and (iii) to use this type information to increase code coverage. The approach leads to up to 87.8 % more branch coverage, improved mutation scores, and to type information of similar quality to that produced by other state-of-the-art type-inference tools.

cs.SE

MigrateLib: a tool for end-to-end Python library migration

Library migration is the process of replacing a library with a similar one in a software project. Manual library migration is time consuming and error prone, as it requires developers to understand the Application Programming Interfaces (API) of both libraries, map equivalent APIs, and perform the necessary code transformations. Due to the difficulty of the library migration process, most of the existing automated techniques and tooling stop at the API mapping stage or support a limited set of libraries and code transformations. In this paper, we develop an end-to-end solution that can automatically migrate code between any arbitrary pair of Python libraries that provide similar functionality. Due to the promising capabilities of Large Language Models (LLMs) in code generation and transformation, we use LLMs as the primary engine for migration. Before building the tool, we first study the capabilities of LLMs for library migration on a benchmark of 321 real-world library migrations. We find that LLMs can effectively perform library migration, but some post-processing steps can further improve the performance. Based on this, we develop MigrateLib, a command line application that combines the power of LLMs, static analysis, and dynamic analysis to provide accurate library migration. We evaluate MigrateLib on 717 real-world Python applications that are not from our benchmark. We find that MigrateLib can migrate 32% of the migrations with complete correctness. Of the remaining migrations, only 14% of the migration-related changes are left for developers to fix for more than half of the projects.

cs.SE

Constraint Decay: The Fragility of LLM Agents in Backend Code Generation

Large Language Model (LLM) agents demonstrate strong performance in autonomous code generation under loose specifications. However, production-grade software requires strict adherence to structural constraints, such as architectural patterns, databases, and object-relational mappings. Existing benchmarks often overlook these non-functional requirements, rewarding functionally correct but structurally arbitrary solutions. We present a systematic study evaluating how well agents handle structural constraints in multi-file backend generation. By fixing a unified API contract across 80 greenfield generation tasks and 20 feature-implementation tasks spanning eight web frameworks, we isolate the effect of structural complexity using a dual evaluation with end-to-end behavioral tests and static verifiers. Our findings reveal a phenomenon of constraint decay: as structural requirements accumulate, agent performance exhibits a substantial decline. Evaluated configurations lose 27.28 points on average in assertion pass rates from baseline to fully specified tasks. Framework sensitivity analysis exposes performance disparities: mid-tier models succeed in minimal, explicit frameworks (e.g., Flask) but perform substantially worse on average in convention-heavy environments (e.g., FastAPI, Django). Finally, error analysis identifies data-layer defects (e.g., incorrect query composition and ORM runtime violations) as the leading root causes. This work highlights that jointly satisfying functional and structural requirements remains a key open challenge for coding agents.

cs.SE