Decision Support for Selecting Blockchain-Based Application Design Patterns With Layered Taxonomy and Quality Attributes

Background: Along with the rapid development and widespread adoption of blockchain technology, many common practices have been summarized into blockchain-based design patterns for application development. However, the numerous and scattered patterns may cause confusion among practitioners. Therefore, adopting appropriate patterns to meet various requirements has become a major challenge, as it requires deep development experience and blockchain technology knowledge. Objective: To address this problem, this paper proposes a decision-support solution to assist with the selection of design patterns during the blockchain-based application development, including a layered taxonomy of design patterns, mappings of quality attributes with the patterns, and a decision model incorporating the taxonomy and mappings. Method: We collected 72 distinct and state-of-the-art design patterns via a Systematic Literature Review (SLR) to establish a layered taxonomy, and 18 unified quality attribute metrics were proposed for blockchain-based pattern assessment and mapping establishment. Based on the pattern taxonomy and quality attribute mappings, we developed a decision model that can provide intuitive guidance for pattern selection. Results: The proposed solution was evaluated through a case study in a seafood supply chain, in which we examined how well the decision model could help identify design flaws and provide reasonable solutions. Additionally, interviews and a questionnaire-based survey were conducted to measure the completeness, correctness, and usefulness of the proposed decision model. The evaluation results indicate that the proposed decision-support solution provides developers with comprehensive guidance, facilitates targeted decision making, and supports intuitive understanding. Conclusions: Our decision-support solution can improve the development efficiency of blockchain-based applications, especially in addressing potential design flaws, achieving targeted quality attributes, and reducing development costs.