Francesco Salzano

The digital transformation of agrifood systems demands an integrated infrastructure to ensure traceability, trust, and intelligent decision-making across complex and heterogeneous value chains. METROFOOD-IT, a large-scale national research infrastructure in food metrology aligned with the ESFRI METROFOOD-RI, addresses these challenges by combining advanced experimental facilities with a comprehensive digital ecosystem. This paper focuses on the IT kernel of METROFOOD-IT and presents an integrated architectural model that brings together four key technological paradigms: data acquisition through Internet of Things (IoT) and laboratory infrastructures, an Open Data Platform for interoperability and sharing, blockchain-based notarization for integrity and provenance, and Artificial Intelligence (AI) for knowledge extraction and decision support. Rather than describing these components in isolation, the paper abstracts from their implementation within the Italian National Recovery and Resilience Plan (NRRP) project METROFOOD-IT to distill a coherent and reusable architectural pattern in which data management, trust enforcement, and intelligent analytics are tightly coupled. Five explicit design principles are identified and articulated: federated data with centralized metadata, selective on-chain anchoring, user-unobtrusive trust infrastructure, explainability as a first-class architectural concern, and machine learning as the backbone of decision-making. Two empirical case studies—one centered on explainable AI for hyperspectral crop nitrogen assessment and the other on IoT-driven sustainable agriculture monitoring secured by distributed ledger technology—serve a dual role: they motivate and shape the architectural pattern, and they exemplify the operational regimes the resulting design supports. A reference deployment on the Ethereum Sepolia public test network, grounded on an IBM Power E1050 and IBM Storage Scale enterprise substrate, provides quantitative evidence for the proposed hybrid on-chain/off-chain pattern with streaming hash-only notarization. The architecture illustrates how research infrastructures can evolve into integrated digital platforms that enable transparent, verifiable, and scalable agrifood systems, and offers a foundation for generalizable design principles in data-intensive and trust-sensitive settings.

The rapid adoption of blockchain technology highlighted the importance of ensuring the security of smart contracts due to their critical role in automated business logic execution on blockchain platforms. This paper provides an empirical evaluation of automated vulnerability analysis tools specifically designed for Solidity smart contracts. Leveraging the extensive SmartBugs 2.0 framework, which includes 20 analysis tools, we conducted a comprehensive assessment using an annotated dataset of 2,182 instances, manually labeled at the line level with vulnerability labels. Our evaluation highlights the detection effectiveness of these tools in detecting various types of vulnerabilities, as categorized by the DASP TOP 10 taxonomy. We evaluated the efficacy of a Large Language Model-based detection method on two popular datasets. In this case, we obtained inconsistent results with the two datasets, showing unreliable detection when analyzing real-world smart contracts. Our study identifies significant variations in the accuracy and reliability of different tools and demonstrates the advantages of combining multiple detection methods to improve vulnerability identification. We identified a set of 3 tools that, combined, achieve up to 76.78% found vulnerabilities, taking less than one minute to run, on average. This study contributes to the field by releasing the largest dataset of manually analyzed smart contracts with line-level vulnerability annotations and by conducting the largest empirical evaluation of tools to date.

AI-based coding agents are increasingly integrated into software development workflows, collaborating with developers to create pull requests (PRs). Despite their growing adoption, the role of human-agent collaboration in software testing remains poorly understood. This paper presents an empirical study of 6,582 human-agent PRs (HAPRs) and 3,122 human PRs (HPRs) from the AIDev dataset. We compare HAPRs and HPRs along three dimensions: (i) testing frequency and extent, (ii) types of testing-related changes (code-and-test co-evolution vs. test-focused), and (iii) testing quality, measured by test smells. Our findings reveal that, although the likelihood of including tests is comparable (42.9% for HAPRs vs. 40.0% for HPRs), HAPRs exhibit a larger extent of testing, nearly doubling the test-to-source line ratio found in HPRs. While test-focused task distributions are comparable, HAPRs are more likely to add new tests during co-evolution (OR = 1.79), whereas HPRs prioritize modifying existing tests. Finally, although some test smell categories differ statistically, negligible effect sizes suggest no meaningful differences in quality. These insights provide the first characterization of how human-agent collaboration shapes testing practices.

In this paper, we investigate the strategies adopted by Solidity developers to fix security vulnerabilities in smart contracts. Vulnerabilities are categorized using the DASP TOP 10 taxonomy, and fixing strategies are extracted from 364 commits collected from open-source Solidity projects on GitHub. Each commit was selected through a two-phase process: an initial filter using natural language processing techniques, followed by manual validation. We assessed whether these fixes adhere to established academic guidelines. Our analysis shows that 60.55% of the commits aligned with at least one literature-based recommendation, particularly for well-documented vulnerability types such as Reentrancy and Arithmetic. However, adherence dropped significantly for categories like Denial of Service, Time Manipulation, and Bad Randomness, highlighting gaps between academic best practices and real-world developer behavior. From the remaining 143 non-aligned commits, we identified 27 novel fixing strategies not previously discussed in the literature. To evaluate their quality, we conducted a structured questionnaire involving 9 experts from both academia and industry. Their feedback indicated high perceived effectiveness of the new fixes, especially for vulnerabilities like Reentrancy and Unchecked Return Values. Generalizability received more varied responses, suggesting context-specific applicability. Finally, we performed a post-fix evolution analysis on over 6700 subsequent commits to assess the long-term stability of the fixes. Most patches remained unchanged, confirming their persistence in production code. Our findings offer practical insights into how vulnerabilities are fixed in smart contracts today, reveal promising emerging patterns, and help bridge the gap between academic guidelines and developer practices.

Smart Contracts are critical components of blockchain ecosystems, with Solidity as the dominant programming language. While LLMs excel at general-purpose code generation, the unique constraints of Smart Contracts, such as gas consumption, security, and determinism, raise open questions about the reliability of LLM-generated Solidity code. Existing studies lack a comprehensive evaluation of these critical functional and non-functional properties. We benchmark four state-of-the-art models under zero-shot and retrieval-augmented generation settings across 500 real-world functions. Our multi-faceted assessment employs code similarity metrics, semantic embeddings, automated test execution, gas profiling, and cognitive and cyclomatic complexity analysis. Results show that while LLMs produce code with high semantic similarity to real contracts, their functional correctness is low: only 20% to 26% of zero-shot generations behave identically to ground-truth implementations under testing. The generated code is consistently simpler, with significantly lower complexity and gas consumption, often due to omitted validation logic. Retrieval-Augmented Generation markedly improves performance, boosting functional correctness by up to 45% and yielding more concise and efficient code. Our findings reveal a significant gap between semantic similarity and functional plausibility in LLM-generated Smart Contracts. We conclude that while RAG is a powerful enhancer, achieving robust, production-ready code generation remains a substantial challenge, necessitating careful expert validation.

The rapid adoption of blockchain technology highlighted the importance of ensuring the security of smart contracts due to their critical role in automated business logic execution on blockchain platforms. This paper provides an empirical evaluation of automated vulnerability analysis tools specifically designed for Solidity smart contracts. Leveraging the extensive SmartBugs 2.0 framework, which includes 20 analysis tools, we conducted a comprehensive assessment using an annotated dataset of 2,182 instances we manually annotated with line-level vulnerability labels. Our evaluation highlights the detection effectiveness of these tools in detecting various types of vulnerabilities, as categorized by the DASP TOP 10 taxonomy. We evaluated the effectiveness of a Large Language Model-based detection method on two popular datasets. In this case, we obtained inconsistent results with the two datasets, showing unreliable detection when analyzing real-world smart contracts. Our study identifies significant variations in the accuracy and reliability of different tools and demonstrates the advantages of combining multiple detection methods to improve vulnerability identification. We identified a set of 3 tools that, combined, achieve up to 76.78\% found vulnerabilities taking less than one minute to run, on average. This study contributes to the field by releasing the largest dataset of manually analyzed smart contracts with line-level vulnerability annotations and the empirical evaluation of the greatest number of tools to date.

In recent years, optical imaging and efficient computational approaches have improved the ability to analyse and understand biological phenomena. Immunofluorescence (IF) is a widely used immunochemical technique that provides information about protein localisation and expression levels. However, the manual analysis of IF images can present important limitations, such as operator workload and interpretative bias. Thus, the development of automated tools for IF signal computation is crucial. Several software programs have been proposed to address this challenge, but there is still a need for more accurate and reliable systems. In this work, we present Q-IF, a software for automatically measuring cellular IF signals with an intuitive and easy-to-use interface. We describe the software and validate its results in different biological scenarios using SH-SY5Y neuroblastoma cells, human fibroblasts, and rat brain sections. The Q-IF system automatically carries out the entire process, from IF signal quantification to statistical analysis, thus evading operator biases and speeding up the analysis workflow. Our results demonstrate the accuracy and reliability of the Q-IF system, highlighting its potential as a valuable tool for IF analysis in biological research.

Addressing the critical challenges of resource inefficiency and environmental impact in the agrifood sector, this study explores the integration of Internet of Things (IoT) technologies with IOTA’s Tangle, a Distributed Ledger Technology (DLT). This integration aims to enhance sustainable agricultural practices, using rice cultivation as a case study of high relevance and reapplicability given its importance in the food chain and the high irrigation requirement of its cultivation. The approach employs sensor-based intelligent irrigation systems to optimize water efficiency. These systems enable real-time monitoring of agricultural parameters through IoT sensors. Data management is facilitated by IOTA’s Tangle, providing secure and efficient data handling, and integrated with MongoDB, a Database Management System (DBMS), for effective data storage and retrieval. The collaboration between IoT and IOTA led to significant reductions in resource consumption. Implementing sustainable agricultural practices resulted in a 50% reduction in water usage, 25% decrease in nitrogen consumption, and a 50% to 70% reduction in methane emissions. Additionally, the system contributed to lower electricity consumption for irrigation pumps and generated comprehensive historical water depth records, aiding future resource management decisions. This study concludes that the integration of IoT with IOTA’s Tangle presents a highly promising solution for advancing sustainable agriculture. This approach significantly contributes to environmental conservation and food security. Furthermore, it establishes that DLTs like IOTA are not only viable but also effective for real-time monitoring and implementation of sustainable agricultural practices.