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.

Nowadays, blockchain technology and smart contracts (SCs) spread as hot computer science and finance topics, catching the interest of a heterogeneous range of users. Thinking about blockchain technologies, users can choose between a broad set of alternatives, like Bitcoin, Ethereum, Hyperledger Fabric, Cardano, and many others. The choice of a blockchain platform over another relies on the user’s purpose. For what concerns smart contracts programming, Ethereum is still the most popular platform despite other popular blockchain technologies recently increasing their popularity for SCs development, such as Algorand, Stellar, Hyperledger Fabric, and many others. Such a tremendous number of alternatives makes it difficult to keep track of blockchain trends and use cases of the different technologies. This work explores users’ most discussed topics and opinions about popular blockchain technologies, focusing on smart contracts development, taking advantage of topic modeling techniques, and in particular, taking advantage of the BERT model. The primary data source is Reddit, which was proven to provide different and exciting blockchain topics. The chosen blockchain platform are four of the most popular platforms for SCs development. These technologies include Ethereum, Stellar, Algorand, and Hyperledger Fabric. The analysis of 14.156 sub-reddits highlighted several exciting topics, such as smart contracts auditing, currency inflation, comparisons between crypto, and general blockchain trends. The most present arguments concern Stellar trends, comparisons between Stellar and Ripple, which Wallet to use to store tokens and crypto, SCs auditing, and Ethereum trends.

Smart Contracts are general-purpose programs that provide a higher level of security than traditional contracts and reduce other transaction costs associated with the bargaining practice, as they are executed in a Blockchain infrastructure. Developers use smart contracts to build their tokens and set up gambling games, crowd sales, ICO, and many others domains of application. The security of Smart Contracts is also crucial, as SCs at the very core level, move money. In recent years, researchers have provided a set of known vulnerabilities that afflict SCs. This study analyzed the relationship between the SC domain of application, domain category, and known vulnerabilities. We categorized the SC using the topic modeling on a curated dataset of SC annotated with know vulnerabilities. Indeed, we found that a certain category of SC is strongly associated with specific vulnerabilities.