For the development of blockchain smart contracts, a structured approach based on the principles of the Model Driven Architecture can be beneficial and facilitate the implementation of smart contracts. This paper presents such an approach, which, in combination with Unified Modeling Language (UML) Class and State machine diagrams, allows the smart contract structure and behavior logic to be modeled in several abstraction layers. This paper delves into details on how the model-to-model transformations from the specified Blockchain Platform Independent Model (PIM) with specified state-like behavior can be used to produce a Solidity Platform Specific Model (PSM). Subsequently, we elaborate on how the Solidity PSM is used for Solidity smart contract code generation by employing model-to-text transformations. The paper also demonstrates the process of our proposed transformations and code generation using smart contract code examples from Solidity documentation. Based on the examples, a Blockchain PIM is specified and transformed to Solidity PSM, which is then used for Solidity smart contract code generation. The generated smart contract code is then compiled, deployed on the Ethereum blockchain JavaScript virtual machine, and compared to the original smart contract code in terms of Solidity code metrics, similarity scores, and execution costs. The evaluation results indicate that our approach could be successfully used to model and later generate smart contract code.

Federated learning is a branch of machine learning where a shared model is created in a decentralized and privacy-preserving fashion, but existing approaches using blockchain are limited by tailored models. We consider the possibility to extend a set of supported models by introducing the oracle service and exploring the usability of blockchain-based architecture. The investigated architecture combines an oracle service with a Hyperledger Fabric chaincode. We compared two logistic regression implementations in Go language—a pure chaincode and an oracle service—at various data (2–32 k instances) and network (3–13 peers) sizes. Experiments were run to assess the performance of blockchain-based model inference using 2D synthetic and EEG eye state datasets for a supervised machine learning detection task. The benchmarking results showed that the impact on performance is acceptable with the median overhead of oracle service reaching 2–4%, depending on the dimensionality of the dataset. The overhead tends to diminish at large dataset sizes with the runtime depending on the network size linearly, where additional peers increased the runtime by 6.3 and 6.6 s for 2D and EEG datasets, respectively. Demonstrated negligible difference between implementations justifies the flexible choice of model in the blockchain-based federated learning and other machine learning applications.

Gamification is widely used in various domains as it helps increase user motivation and engagement. Therefore, it is important to ensure that the gamified system meets usability requirements and conforms to user experience and human-centred design guidelines, as poor usability may lead to undesired side-effects, such as a loss of user retention. Unfortunately, it is not entirely clear what usability guidelines are related to gamified systems and how they depend on the gamification target audience and on the gamified system application domain. This paper provides a systematic review of usability, user experience and human-centred design guidelines for gamified systems. A systematic search was performed in Web of Science, Scopus, IEEE Xplore, ScienceDirect, and ACM Digital Library databases, following the PRISMA guidelines. The results show that most of the recommendations listed in analysed papers are related to improving learnability of gamified systems. Furthermore, the system application domain is generally viewed as very important when making usability-related decisions. The key distinctive gamification application domains are medicine, education, and business. The findings may be useful to usability and gamification researchers, as well as the developers and owners of gamified systems who want to grasp the state of research into the specifics of gamified systems usability.

Blockchain smart contracts can support the decentralisation of business processes, but due to smart contracts’ specifics, their development is a complicated process. Introducing model-driven development principles in smart contract development can facilitate requirement specification, design, and implementation activities. This paper presents a model-driven development method MDAsmartCD (Model-Driven Architecture-based Smart Contract Development) to alleviate smart contract development by supporting the complete MDA life cycle, covering the definition of Computation-Independent Model, Platform-Independent Model, and two instances of Platform-Specific Models. In MDAsmartCD, model transformations (model-to-model and model-to-text) are used to produce smart contract code in the Hyperledger Fabric platform Go and the Ethereum platform Solidity programming languages. The method application was demonstrated by implementing the smart contract for the hackathon solution and executing the generated Solidity and Go smart contracts in the workflow of issuing certificates for hackathon participants. During the execution of the workflow, both deployed smart contracts behaved identically and recorded analogous results in respective blockchain data storages. This demonstrated that the MDAsmartCD method enables the generation of compilable and executable smart contract code, ready for deployment on a blockchain platform.