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Researcher profile

Olivier Bouissou

Olivier Bouissou contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Numerical AnalysisProgramming LanguagesSoftware EngineeringMachine Learningmath.NAmath.OCSystems and Control

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Published work

4 published item(s)

preprint2026arXiv

DafnyPro: LLM-Assisted Automated Verification for Dafny Programs

We present DafnyPro, an inference-time framework that enhances LLMs for generating verification annotations in Dafny. DafnyPro comprises three key components: a diff-checker that prevents modifications to base program logic, a pruner that removes unnecessary invariants, and a hint-augmentation system that retrieves and applies predefined, problem-independent proof strategies. We evaluate DafnyPro using Claude Sonnet 3.5 and 3.7 on four benchmarks: Clover, MBPP-Dafny, HumanEval-Dafny, and DafnyBench, achieving consistent performance gains in all cases. Notably, on DafnyBench, the most challenging benchmark, Claude Sonnet 3.5 enhanced with DafnyPro achieves 86% correct proofs, a 16 pp improvement over the base model. We also fine-tune two Qwen models on training data derived from verification attempts by larger models enhanced with DafnyPro. Our 7B and 14B models achieve 68% and 70% correct proofs on DafnyBench, respectively, demonstrating that smaller models can maintain high verification accuracy.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Teaching LLMs Program Semantics via Symbolic Execution Traces

We introduce an evaluation framework of 500 C verification tasks across five property types (memory safety, overflow, termination, reachability, data races) built on SV-COMP 2025, and evaluate 14 models across six families. We find that high overall accuracy masks a critical weakness: while most models reliably confirm properties hold, violation detection varies widely and degrades sharply with program length. To close this gap, we train on formal verification artifacts: running the Soteria symbolic execution engine on generic open-source C code and using the resulting traces for continued pretraining of Qwen3-8B. Just ${\sim}$3,000 bug traces combined with chain-of-thought reasoning at inference time improve violation detection by over 17 percentage points, producing one of the most balanced accuracy profiles among evaluated models. On violation detection, the trained 8B model outperforms the 4$\times$ larger Qwen3-32B without thinking and approaches it in overall accuracy. The interaction between trace training and chain-of-thought is superadditive: neither alone provides meaningful gains, but their combination does. Improvements transfer across all five property types, including ones the training traces do not target. Our 28 configurations confirm the gains stem from trace semantics, not code volume, and that trace curation and format matter.

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preprint2013arXiv

Computing Flowpipe of Nonlinear Hybrid Systems with Numerical Methods

Modern control-command systems often include controllers that perform nonlinear computations to control a physical system, which can typically be described by an hybrid automaton containing high-dimensional systems of nonlinear differential equations. To prove safety of such systems, one must compute all the reachable sets from a given initial position, which might be uncertain (its value is not precisely known). On linear hybrid systems, efficient and precise techniques exist, but they fail to handle nonlinear flows or jump conditions. In this article, we present a new tool name HySon which computes the flowpipes of both linear and nonlinear hybrid systems using guaranteed generalization of classical efficient numerical simulation methods, including with variable integration step-size. In particular, we present an algorithm for detecting discrete events based on guaranteed interpolation polynomials that turns out to be both precise and efficient. Illustrations of the techniques developed in this article are given on representative examples.

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preprint2010arXiv

Abstract Fixpoint Computations with Numerical Acceleration Methods

Static analysis by abstract interpretation aims at automatically proving properties of computer programs. To do this, an over-approximation of program semantics, defined as the least fixpoint of a system of semantic equations, must be computed. To enforce the convergence of this computation, widening operator is used but it may lead to coarse results. We propose a new method to accelerate the computation of this fixpoint by using standard techniques of numerical analysis. Our goal is to automatically and dynamically adapt the widening operator in order to maintain precision.

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