Incorporation of Machine Learning in the Selection of Event Patterns for Smart Contract Modelling

Abstract

Event patterns are used to model interactions between smart contracts and their environment, improving the efficiency, security, and flexibility of applications based on blockchain technologies. Event pattern-driven smart contract modelling is an emerging trend. Defining a new pattern or selecting a pre-existing one from a limited catalog is a fundamental activity for any analysis model that utilizes event patterns. This study presents a proposal to manage a catalog of event patterns that can be applied to the conceptual modelling of smart contracts and incorporates machine learning techniques to optimize pattern selection. To test this proposal, a prototype application called PatCat (Pattern Catalogue) was developed, using a decentralized electronic voting application as a case study. The incorporation of patterns at the beginning of the modelling process simplifies and clarifies the elicitation of requirements, among other benefits, while the use of machine learning accelerates the description of the problem situation. Consequently, a specialized application for managing a catalog of event patterns, supported by machine learning techniques, proves useful in standardizing and streamlining smart contract modelling tasks.