arXiv subjects

Sabina Rossi

Publications and source records attributed to Sabina Rossi.

13 recordsLinked to original sources

arXiv2026

A Lumpability-Driven Taxonomy of Strong and Weak Stochastic Bisimilarities with Their Congruence Properties

We study the relationships among the stochastic bisimulation-style equivalences over PEPA - Performance Evaluation Process Algebra definable according to the well known notions of lumpability for the continuous-time Markov chains (CTMCs) underlying process terms. Lumpability is a central tool in the analysis of a CTMC, because it results in aggregations of the state space enjoying properties that are useful for efficiently computing the state probability distribution of the original chain. At the level of process terms, various stochastic bisimilarities accounting for activity types and cumulative rates can be defined over PEPA, which induce different kinds of lumping. Since the formalisations of some of them are scattered across the literature, where they appear under different, and sometimes clashing, names, we collect them within a single, uniform framework, renaming each bisimilarity in a consistent way after the kind of lumping it induces. We present strong and weak variants of what we call ordinary, exact, and strict bisimilarities and show that they respectively induce ordinary, exact, and strict lumpings. We then organise the six bisimilarities into a taxonomy establishing all and only the inclusions holding among them. We also analyse how the taxonomy changes in three special cases: process terms whose underlying CTMCs are time reversible, process terms with no activities of unobservable types, and process terms with no recursion. The paper concludes by investigating the compositionality properties of the six bisimilarities. Some of them are not congruences with respect to the prefix and/or choice operators of PEPA. In that case we single out either a set of process terms over which congruence with respect to those operators is achieved, or the coarsest congruence with respect to them that is contained in the considered bisimilarity.

cs.LO↗

arXiv2026

Reentrancy Detection in the Age of LLMs

Reentrancy remains one of the most critical classes of vulnerabilities in Ethereum smart contracts, yet widely used detection tools and datasets continue to reflect outdated patterns and obsolete Solidity versions. This paper adopts a dependability-oriented perspective on reentrancy detection in Solidity 0.8+, assessing how reliably state-of-the-art static analyzers and AI-based techniques operate on modern code by putting them to the test on two fronts. We construct two manually verified benchmarks: an Aggregated Benchmark of 432 real-world contracts, consolidated and relabeled from prior datasets, and a Reentrancy Scenarios Dataset (RSD) of \chadded{143} handcrafted minimal working examples designed to isolate and stress-test individual reentrancy patterns. We then evaluate 12 formal-methods-based tools, 10 machine-learning models, and 9 large language models. On the Aggregated Benchmark, traditional tools and ML models achieve up to 0.87 F1, while the best LLMs reach 0.96 in a zero-shot setting. On the RSD, most tools fail on multiple scenarios, the top performer achieving an F1 of 0.76, whereas the strongest model attains 0.82. Overall, our results indicate that leading LLMs outperform the majority of existing detectors, highlighting concerning gaps in the robustness and maintainability of current reentrancy-analysis tools.

cs.CR↗

arXiv2025

Exact Persistent Stochastic Non-Interference

Persistent Stochastic Non-Interference (PSNI) was introduced to capture a quantitative security property in stochastic process algebras, ensuring that a high-level process does not influence the observable behaviour of a low-level component, as formalised via lumpable bisimulation. In this work, we revisit PSNI from a performance-oriented perspective and propose a new characterisation based on a refined behavioural relation. We introduce \emph{weak-exact equivalence}, which extends exact equivalence with a relaxed treatment of internal (\(τ\)) actions, enabling precise control over quantitative observables while accommodating unobservable transitions. Based on this, we define \emph{Exact PSNI} (EPSNI), a variant of PSNI characterised via weak-exact equivalence. We show that EPSNI admits the same bisimulation-based and unwinding-style characterisations as PSNI, and enjoys analogous compositionality properties. These results confirm weak-exact equivalence as a robust foundation for reasoning about non-interference in stochastic systems.

cs.PF↗

arXiv2025

Assessing Code Understanding in LLMs

We present an empirical evaluation of Large Language Models in code understanding associated with non-trivial, semantic-preserving program transformations such as copy propagation or constant folding. Our findings show that LLMs fail to judge semantic equivalence in approximately 41\% of cases when no context is provided and in 29\% when given a simple generic context. To improve accuracy, we advocate integrating LLMs with code-optimization tools to enhance training and facilitate more robust program understanding.

cs.SE↗

arXiv2025

Noninterference Analysis of Reversible Systems: An Approach Based on Branching Bisimilarity

The theory of noninterference supports the analysis of information leakage and the execution of secure computations in multi-level security systems. Classical equivalence-based approaches to noninterference mainly rely on weak bisimulation semantics. We show that this approach is not sufficient to identify potential covert channels in the presence of reversible computations. As illustrated via a database management system example, the activation of backward computations may trigger information flows that are not observable when proceeding in the standard forward direction. To capture the effects of back-and-forth computations, it is necessary to switch to a more expressive semantics, which has been proven to be branching bisimilarity in a previous work by De Nicola, Montanari, and Vaandrager. In this paper we investigate a taxonomy of noninterference properties based on branching bisimilarity along with their preservation and compositionality features, then we compare it with the taxonomy of Focardi and Gorrieri based on weak bisimilarity.

cs.CR↗

arXiv2024

Vulnerability Detection in Ethereum Smart Contracts via Machine Learning: A Qualitative Analysis

Smart contracts are central to a myriad of critical blockchain applications, from financial transactions to supply chain management. However, their adoption is hindered by security vulnerabilities that can result in significant financial losses. Most vulnerability detection tools and methods available nowadays leverage either static analysis methods or machine learning. Unfortunately, as valuable as they are, both approaches suffer from limitations that make them only partially effective. In this survey, we analyze the state of the art in machine-learning vulnerability detection for Ethereum smart contracts, by categorizing existing tools and methodologies, evaluating them, and highlighting their limitations. Our critical assessment unveils issues such as restricted vulnerability coverage and dataset construction flaws, providing us with new metrics to overcome the difficulties that restrain a sound comparison of existing solutions. Driven by our findings, we discuss best practices to enhance the accuracy, scope, and efficiency of vulnerability detection in smart contracts. Our guidelines address the known flaws while at the same time opening new avenues for research and development. By shedding light on current challenges and offering novel directions for improvement, we contribute to the advancement of secure smart contract development and blockchain technology as a whole.

cs.CR↗

arXiv2024

Smart Contract Languages: a comparative analysis

Smart contracts have played a pivotal role in the evolution of blockchains and Decentralized Applications (DApps). As DApps continue to gain widespread adoption, multiple smart contract languages have been and are being made available to developers, each with its distinctive features, strengths, and weaknesses. In this paper, we examine the smart contract languages used in major blockchain platforms, with the goal of providing a comprehensive assessment of their main properties. Our analysis targets the programming languages rather than the underlying architecture: as a result, while we do consider the interplay between language design and blockchain model, our main focus remains on language-specific features such as usability, programming style, safety and security. To conduct our assessment, we propose an original benchmark which encompasses a wide, yet manageable, spectrum of key use cases that cut across all the smart contract languages under examination.

cs.CR↗

arXiv2022

Neural Networks Reduction via Lumping

The increasing size of recently proposed Neural Networks makes it hard to implement them on embedded devices, where memory, battery and computational power are a non-trivial bottleneck. For this reason during the last years network compression literature has been thriving and a large number of solutions has been been published to reduce both the number of operations and the parameters involved with the models. Unfortunately, most of these reducing techniques are actually heuristic methods and usually require at least one re-training step to recover the accuracy. The need of procedures for model reduction is well-known also in the fields of Verification and Performances Evaluation, where large efforts have been devoted to the definition of quotients that preserve the observable underlying behaviour. In this paper we try to bridge the gap between the most popular and very effective network reduction strategies and formal notions, such as lumpability, introduced for verification and evaluation of Markov Chains. Elaborating on lumpability we propose a pruning approach that reduces the number of neurons in a network without using any data or fine-tuning, while completely preserving the exact behaviour. Relaxing the constraints on the exact definition of the quotienting method we can give a formal explanation of some of the most common reduction techniques.

cs.LG↗

arXiv2018

Persistent Stochastic Non-Interference

In this paper we present an information flow security property for stochastic, cooperating, processes expressed as terms of the Performance Evaluation Process Algebra (PEPA). We introduce the notion of Persistent Stochastic Non-Interference (PSNI) based on the idea that every state reachable by a process satisfies a basic Stochastic Non-Interference (SNI) property. The structural operational semantics of PEPA allows us to give two characterizations of PSNI: the first involves a single bisimulation-like equivalence check, while the second is formulated in terms of unwinding conditions. The observation equivalence at the base of our definition relies on the notion of lumpability and ensures that, for a secure process P, the steady state probability of observing the system being in a specific state P' is independent from its possible high level interactions.

cs.PF↗

arXiv2001

On Modular Termination Proofs of General Logic Programs

We propose a modular method for proving termination of general logic programs (i.e., logic programs with negation). It is based on the notion of acceptable programs, but it allows us to prove termination in a truly modular way. We consider programs consisting of a hierarchy of modules and supply a general result for proving termination by dealing with each module separately. For programs which are in a certain sense well-behaved, namely well-moded or well-typed programs, we derive both a simple verification technique and an iterative proof method. Some examples show how our system allows for greatly simplified proofs.

cs.LO↗

arXiv2001

Semantics and Termination of Simply-Moded Logic Programs with Dynamic Scheduling

In logic programming, dynamic scheduling refers to a situation where the selection of the atom in each resolution (computation) step is determined at runtime, as opposed to a fixed selection rule such as the left-to-right one of Prolog. This has applications e.g. in parallel programming. A mechanism to control dynamic scheduling is provided in existing languages in the form of delay declarations. Input-consuming derivations were introduced to describe dynamic scheduling while abstracting from the technical details. In this paper, we first formalise the relationship between delay declarations and input-consuming derivations, showing in many cases a one-to-one correspondence. Then, we define a model-theoretic semantics for input-consuming derivations of simply-moded programs. Finally, for this class of programs, we provide a necessary and sufficient criterion for termination.

cs.LO↗

arXiv2001

Properties of Input-Consuming Derivations

We study the properties of input-consuming derivations of moded logic programs. Input-consuming derivations can be used to model the behavior of logic programs using dynamic scheduling and employing constructs such as delay declarations. We consider the class of nicely-moded programs and queries. We show that for these programs a weak version of the well-known switching lemma holds also for input-consuming derivations. Furthermore, we show that, under suitable conditions, there exists an algebraic characterization of termination of input-consuming derivations.

cs.PL↗

arXiv2000

Sequence-Based Abstract Interpretation of Prolog

Many abstract interpretation frameworks and analyses for Prolog have been proposed, which seek to extract information useful for program optimization. Although motivated by practical considerations, notably making Prolog competitive with imperative languages, such frameworks fail to capture some of the control structures of existing implementations of the language. In this paper we propose a novel framework for the abstract interpretation of Prolog which handles the depth-first search rule and the cut operator. It relies on the notion of substitution sequence to model the result of the execution of a goal. The framework consists of (i) a denotational concrete semantics, (ii) a safe abstraction of the concrete semantics defined in terms of a class of post-fixpoints, and (iii) a generic abstract interpretation algorithm. We show that traditional abstract domains of substitutions may easily be adapted to the new framework, and provide experimental evidence of the effectiveness of our approach. We also show that previous work on determinacy analysis, that was not expressible by existing abstract interpretation frameworks, can be seen as an instance of our framework.

cs.LO↗