Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.02814

VeriPy Source-Preserving Verification and Compatibility Checking for Python Components

Naing Oo Lwin
View original source ↗Read source document ↗Export metadata ↓

Abstract

Keeping a Python program and its formal guarantees aligned is a continuing maintenance problem. Specifications must describe the implementation that actually runs, and updates must preserve the behavior on which existing callers depend. VeriPy brings these obligations into a common workflow for annotated Python components. Developers and agents express contracts, invariants, and proof hooks as Python comments, then refine checked auxiliary lemmas using source-located diagnostics. Direct encoders produce Dafny or Lean artifacts while retaining admitted executable bodies and explicit models of their dependencies. A relational product checks whether an updated component still admits old inputs and preserves returned values and modeled exceptions. The resulting workflow connects source-level proof development with functional verification and backward compatibility, recording the assumptions under which each guarantee applies. The code is available at https://github.com/astrio-labs/veripy.

Explore related subjects

cs.SEcs.LOcs.PL

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Naing Oo Lwin. 2026-10-02. VeriPy Source-Preserving Verification and Compatibility Checking for Python Components. https://arxiv.org/abs/2610.02814

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

KEEP EXPLORING

Related papers

arXiv2026

Can we find bugs using LLM-generated oracles?

Unit testing is vital in software development. Typically, a unit test consists of a test prefix and a test oracle which captures the developer's intended behaviour. Traditional test generation tools (e.g. Randoop and Evosuite) often produce oracles that mirror the program's actual behavior rather than the expected one, limiting their ability to automatically detect bugs as users must manually verify if the generated assertions are correct. Recent approaches leverage Large Language Models (LLMs), trained on vast datasets, to generate developer-like code and test cases. Although successful in generating tests, the question of whether such LLM-generated oracles can automatically find bugs, i.e., expected software behavior, remains unanswered. We conduct a controlled experiment to answer this question, by studying LLMs on two tasks, namely, test oracle classification and generation, and assessing whether LLM oracles capture the actual or the expected behavior. The study includes test cases and oracles written by developers and automatically generated for 24 Java repositories. Our findings show that LLM-based test generation approaches mainly capture the actual program behavior making bug detection difficult. We also find that LLMs are better at generating oracles than classifying them. Notably, LLM-generated oracles have a higher fault detection potential than the Evosuite ones.

cs.SE↗
arXiv2026

SELU: A Software Engineering Language Understanding Benchmark

Large Language Models (LLMs) have demonstrated remarkable capabilities in code understanding and generation. However, their effectiveness on non-code Software Engineering (SE) tasks remains underexplored. We present 'Software Engineering Language Understanding' (SELU), the first comprehensive benchmark for evaluating LLMs on 22 SE textual artifacts NLU tasks, spanning from identifying whether a requirement is functional or non-functional to estimating the effort required to implement a development task. SELU covers classification, regression, Named Entity Recognition (NER), and Masked Language Modeling (MLM) tasks, with data drawn from diverse sources such as issue tracking systems and developer forums. We fine-tune 22 open-source LLMs, both generalist and domain-adapted; and prompt two proprietary alternatives using zero-shot a 3-shot prompting strategies. Performance is measured using metrics such as F1-macro, SMAPE, F1-micro, and accuracy, and compared via the Bayesian signed-rank test. Our results show that fine-tuned models across various sizes and architectures perform best, exhibiting high mean performance and low across-task variance. Furthermore, domain adaptation via code-focused pre-training does not yield significant improvements and might even be counterproductive for developer communication tasks.

cs.SE↗
arXiv2026

Mut4All: Fuzzing Compilers via LLM-Synthesized Mutators Learned from Bug Reports

Mutation-based fuzzing is effective for uncovering compiler bugs, but designing high-quality mutators for modern languages with complex constructs (e.g., templates, macros) remains challenging. Existing methods rely heavily on manual design or human-in-the-loop correction, limiting scalability and cross-language generalizability. We present Mut4All, a fully automated, language-agnostic framework that synthesizes mutators using Large Language Models (LLMs) and compiler-specific knowledge from bug reports. It consists of three agents: (1) a mutator invention agent that identifies mutation targets and generates mutator metadata using compiler-related insights; (2) a mutator implementation synthesis agent, fine-tuned to produce initial implementations; and (3) a mutator refinement agent that verifies and corrects the mutators via unit-test feedback. Mut4All processes 1400 bug reports (700 Rust, 700 C++), yielding 444 Rust and 561 C++ mutators at ~$0.08 each via GPT-4o. Our customized fuzzer, using these mutators, finds 62 bugs in Rust compilers (44 new, 32 fixed) and 38 bugs in C++ compilers (17 new, 3 fixed). Mut4All outperforms existing methods in both unique crash detection and coverage, ranking first on Rust and second on C++.

cs.SE↗