We witness a paradigm shift where entities such as software applications, people, businesses and service providers increasingly interact in virtual rather than physical environments, such as social media, social commerce, and the metaverses. A key issue that emerges in such environments is how one entity can trust another. Here, the concept of trust is considered as a meta-requirement, that is, the level of belief a service requestor has that a service provider will provide the service in a way that meets the requestor’s expectations. We refer to the service offering entities as service providers (SPs) and the service requesting entities as service clients (SCs). In this paper, we propose a technique that allows for evaluating trust and assigning reputation to various service providers by considering first their ability to fulfill their clients’ expectations or policies, and second the reputation other service clients have, when acting as recommenders for the aforementioned service providers. In this work, service clients and service providers are considered as virtual entities that coordinate with each other, and may include physical users, avatars, micro-services, software agents, smart contracts or any other distributed inter-networked resource, without making any assumptions as to what a service client or a service provider entity is as long as it participates in an interaction.

The emergence of smart contracts in blockchain environments allows for a wide spectrum of transaction oriented trusted applications to be developed. The majority of smart contracts are implemented as scripts written in specialized machine interpretable languages such as Solidity. However recent efforts allow for more general purpose languages such as GoLang and JavaScript to be used. It is therefore evident, that smart contract applications will quickly become more complex, and an interesting question that arises is how we can ensure that the smart contract code achieves and complies with the required goals and policies set by the system's stakeholders. In this short paper we outline a model-based approach for the automatic generation and deployment of smart contract code by using extended goal models to capture the tasks and policies set by various stakeholders, and by applying a code generator to produce Solidity code that corresponds to the model.