As blockchain systems grow in complexity, secure and efficient smart contract development remains a crucial challenge. Large Language Models (LLMs) like DeepSeek promise significant enhancements in developer productivity through automated code generation, debugging, and testing. This study focuses on Solidity, the dominant language for Ethereum smart contracts, where correctness, gas efficiency, and security are critical to real-world adoption. This study evaluates the capabilities of DeepSeek’s V3 and R1 models, a non-reasoning Mixture-of-Experts architecture and a reasoning-based model trained via reinforcement learning, respectively, in automating Solidity contract generation and testing, as well as identifying and fixing common vulnerabilities. We designed a controlled experimental framework to evaluate both models by generating and analysing a diverse set of smart contracts, including standardised tokens (ERC20, ERC721, ERC1155) and real-world application scenarios (Supply Chain, Token Exchange, Auction). The evaluation is grounded on a multidimensional metric suite covering quality, technical robustness and process characteristics. Vulnerability detection and patching capabilities are tested using predefined vulnerable contracts and guided patch prompts. The analysis spans six levels of prompt complexity and compares the impact of reasoning-based and non-reasoning-based generation strategies. Findings reveal that R1 delivers more accurate and optimised outputs under high complexity, while V3 performs more consistently in simpler tasks with simpler code structures. However, both models exhibit persistent hallucinations, limitations in vulnerability coverage, and inconsistencies due to prompt formulation. The correlation between re-evaluation patterns and output quality suggests that reasoning helps in complex scenarios, although excessive revisions may lead to over-engineered or unstable solutions. Neither model is robust enough to autonomously generate issue-free smart contracts in complex or security-critical scenarios, underscoring the need for human oversight. These findings highlight best practices for integrating LLMs into blockchain development workflows and emphasise the importance of aligning model selection with task complexity and security requirements.

This paper presents a first investigation to join agile blockchain-oriented software development principles with sustainability software design principles. The development of blockchain-oriented software should always be performed in the awareness of the potential effects generated from its use, especially in a long-term life cycle perspective. In other terms in the awareness of its present and future sustainability. By using the principles of sustainability software design and recognized the role of blockchain-oriented Agile methodologies to manage changes in technology and requirements, we present a new Agile method for the development of blockchain-oriented systems that includes sustainability awareness practices within the development phases, in particular in the requirements and the acceptance tests. This allows to deal with blockchain-oriented systems sustainability immediately and during the incremental and iterative development process. The paper describes the process in its phases and provides an example of an application to the supply chain sector.

Summary This article proposes a blockchain oriented platform to guarantee the origin and provenance of food items in a Smart Tourism Region context. Local food and beverage, in fact, can become a good combination to attract tourist and to promote the area provided that their provenance is clearly certified. We designed and developed a blockchain‐based system to manage an agri‐food supply chain for tracking food items. By using smart contracts the platform guarantees transparency, efficiency and trustworthiness. Our system is particularly suitable to manage cold chain since the system interfaces with IoT network devices providing detailed information about data monitoring food such as storage temperature, environment humidity, and GPS data. All involved actors can share data and information in a more efficient, transparent, and tamper proof way than traditional systems. The final consumer can access with transparency to all the agri‐food chain of the purchased product and verify provenance by retrieving all detailed information registered in the blockchain public ledger. The proposed system has been designed according to the ABCDE method, an agile development process recently conceived, to obtain a higher software quality to design a general blockchain system by means software engineering practices. A real case study applied to local products from Sardinia, Italy, is proposed at the end of the article.

Abstract An Initial Coin Offering (ICO) is an innovative way to raise funds and launch a startup. It is also an opportunity to take part in a project, or in a DAO (Decentralized Autonomous Organization). The use of ICO is a global phenomenon that involves many nations and several business categories: ICOs collected over 5.2 billion dollars only in 2017. The success of an ICO is based on the credibility and innovativeness of project proposals. This fund-raising tool contains however some critical issues, such as the use of tokens that have no intrinsic value and do not generate direct liquidity, and the role of investors in the management of the startup. We analyzed if the Lean Startup methodology is helpful to face this critical aspects and we examined some ICOs in which the proposing team states explicitly that a lean startup approach is used.

Teledermatology offers the possibility of improving access to dermatologic care by using telecommunication technologies for connecting several medical centers and enabling to exchange information about skin conditions over a distance. This paper describes the main points of the implementation of a teledermatology project following the so-called smart region paradigm. A case study is presented to describe how the implementation of a virtual organization model using teledermatology is able to promote and facilitate the diagnosis on skin diseases and improve the quality of care for rural and remote areas. Initial results and assessment by general practitioners and dermatologists show that the proposed software platform can be used as a decision support system for dermatological patients.

The spread of the Social Web is influencing the evolution of Semantic Web: the way of producing and consulting information changes, as well as the way people relate themselves with the Internet and the services it gives. Users will participate at first hand to the developing of the Web which therefore becomes interactive. This study considers this feature, trying to link the worlds of Social Media and Semantic Web, with the aim of proposing a semantic classification of the information coming from the Web, which do not always follow a well-defined order and organization. Starting from a precise analysis of the information of the Web through an accurate and meticulous study on how these are presented and used, in order to give a sorted and easily usable data structure, this approach wants to define a taxonomy able to represent knowledge through an iterative combined approach, where top-down and bottom-up analyses are applied on the knowledge domain we want to represent