arXiv subjects

Gerardo Iuliano

Publications and source records attributed to Gerardo Iuliano.

5 recordsLinked to original sources

arXiv2026

MuSe: a Mutation Testing Plugin for the Remix IDE

Mutation testing is a technique to assess the effectiveness of test suites by introducing artificial faults into programs. Although mutation testing plugins are available for many platforms and languages, none is currently available for Remix-IDE, the most widely used Integrated Development Environment for the entire contract development journey, used by users of all knowledge levels, and serves as a learning lab for teaching and experimenting with Ethereum. The quality and security of smart contracts are crucial in blockchain systems, as even minor issues can result in substantial financial losses. This paper proposes MuSe, a mutation testing plugin for the Remix-IDE. MuSe includes traditional, Solidity-specific, and security-oriented mutation operators. Its integration into the Remix-IDE eliminates the need for additional setup and lowers the entry barrier. As a result, developers and researchers can immediately leverage mutation testing to assess the effectiveness of their test suites and identify potential issues in smart contracts. We provide a demo video showing MuSe: https://www.youtube.com/watch?v=MIFk9exTDu0 and its repository: https://github.com/GerardoIuliano/MuSe-Remix-Plugin.

cs.SE↗

arXiv2026

Smart Contract Vulnerabilities, Tools, and Benchmarks: an Updated Systematic Literature Review

Smart contracts are self-executing programs on blockchain platforms like Ethereum, which have revolutionized decentralized finance by enabling trustless transactions and the operation of decentralized applications. Despite their potential, the security of smart contracts remains a critical concern due to their immutability and transparency, which expose them to malicious actors. Numerous solutions for vulnerability detection have been proposed, but it is still unclear which one is the most effective. This paper presents a systematic literature review that explores vulnerabilities in Ethereum smart contracts, focusing on automated detection tools and benchmark evaluation. We reviewed 3,380 studies from five digital libraries and five major software engineering conferences, applying a structured selection process that resulted in 222 high-quality studies. The key results include a hierarchical taxonomy of 192 vulnerabilities grouped into 13 categories, a comprehensive list of 219 detection tools with corresponding functionalities, methods, and code transformation techniques, a mapping between our taxonomy and the list of tools, and a collection of 133 benchmarks used for tool evaluation. We conclude with a discussion about the insights into the current state of Ethereum smart contract security and directions for future research.

cs.SE↗

arXiv2025

Automated Vulnerability Injection in Solidity Smart Contracts: A Mutation-Based Approach for Benchmark Development

The security of smart contracts is critical in blockchain systems, where even minor vulnerabilities can lead to substantial financial losses. Researchers proposed several vulnerability detection tools evaluated using existing benchmarks. However, most benchmarks are outdated and focus on a narrow set of vulnerabilities. This work evaluates whether mutation seeding can effectively inject vulnerabilities into Solidity-based smart contracts and whether state-of-the-art static analysis tools can detect the injected flaws. We aim to automatically inject vulnerabilities into smart contracts to generate large and wide benchmarks. We propose MuSe, a tool to generate vulnerable smart contracts by leveraging pattern-based mutation operators to inject six vulnerability types into real-world smart contracts. We analyzed these vulnerable smart contracts using Slither, a static analysis tool, to determine its capacity to identify them and assess their validity. The results show that each vulnerability has a different injection rate. Not all smart contracts can exhibit some vulnerabilities because they lack the prerequisites for injection. Furthermore, static analysis tools fail to detect all vulnerabilities injected using pattern-based mutations, underscoring the need for enhancements in static analyzers and demonstrating that benchmarks generated by mutation seeding tools can improve the evaluation of detection tools.

cs.SE↗

arXiv2025

How Do Solidity Versions Affect Vulnerability Detection Tools? An Empirical Study

Context: Smart contract vulnerabilities pose significant security risks for the Ethereum ecosystem, driving the development of automated tools for detection and mitigation. Smart contracts are written in Solidity, a programming language that is rapidly evolving to add features and improvements to enhance smart contract security. New versions of Solidity change the compilation process, potentially affecting how tools interpret and analyze smart contract code. Objective: In such a continuously evolving landscape, we aim to investigate the compatibility of detection tools with Solidity versions. More specifically, we present a plan to study detection tools by empirically assessing (i) their compatibility with the Solidity pragma directives, (ii) their detection effectiveness, and (iii) their execution time across different versions of Solidity. Method: We will conduct an exploratory study by running several tools and collecting a large number of real-world smart contracts to create a balanced dataset. We will track and analyze the tool execution through SmartBugs, a framework that facilitates the tool execution and allows the integration of new tools.

cs.SE↗

arXiv2020

Longitudinal modeling of MS patient trajectories improves predictions of disability progression

Research in Multiple Sclerosis (MS) has recently focused on extracting knowledge from real-world clinical data sources. This type of data is more abundant than data produced during clinical trials and potentially more informative about real-world clinical practice. However, this comes at the cost of less curated and controlled data sets. In this work, we address the task of optimally extracting information from longitudinal patient data in the real-world setting with a special focus on the sporadic sampling problem. Using the MSBase registry, we show that with machine learning methods suited for patient trajectories modeling, such as recurrent neural networks and tensor factorization, we can predict disability progression of patients in a two-year horizon with an ROC-AUC of 0.86, which represents a 33% decrease in the ranking pair error (1-AUC) compared to reference methods using static clinical features. Compared to the models available in the literature, this work uses the most complete patient history for MS disease progression prediction.

cs.LG↗