Model‐Driven Full‐Lifecycle Support Reference Framework for Blockchain‐Based Application Development
Abstract
ABSTRACT Blockchain technology has the potential to improve various traditional industries by enabling decentralized, tamper‐resistant data management and transparency. These characteristics, however, make the development of blockchain‐based applications demanding and costly. Model‐driven development (MDD) offers a paradigm by abstracting low‐level details into higher level models, thereby potentially enhancing efficiency and reducing errors. Yet, existing MDD‐based solutions are fragmented and lack comprehensive support for the software development lifecycle (SDLC) in a consistent manner. Moreover, the intrinsic complexity of blockchain and its heterogeneous infrastructures introduces further challenges across SDLC stages, highlighting the need for a unified framework that systematically integrates MDD with blockchain‐specific development. This research proposes an MDD‐based reference framework that supports a consistent modeling process across the entire SDLC of blockchain‐based applications, from requirements to deployment. The framework adapts various modeling techniques to address blockchain‐specific features such as decentralization, one‐time smart‐contract deployment, and immutable data record. The model transformation methods are improved to support the seamless full SDLC development process of blockchain‐based systems. The proposed reference framework is platform independent at modeling level, enabling compatibility with heterogeneous blockchain platforms. Its adaptability was validated through a prototype implementation supporting both Ethereum and Hyperledger Fabric. Further, a comparative case study was conducted on a cross‐border food supply chain. The results demonstrate the feasibility and adaptability of the MDD‐based reference framework, as well as improved efficiency for blockchain‐based system development, which promotes a broader adoption of blockchain technology.
Fields of Study & Subjects
Cloud Computing and Resource Management
Business Process Modeling and Analysis
Scientific Computing and Data Management
Publication details
- Journal / source
- Journal of Software Evolution and Process
- Volume
- 38
- Issue
- 6
- Publication date
- 1 June 2026
- Language
- EN