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Article

A Recommender System for Robust Smart Contract Template Classification

Faculty of Computer and Information Science, University of Ljubljana, 1000 Ljubljana, Slovenia
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Author to whom correspondence should be addressed.
Sensors 2023, 23(2), 639; https://doi.org/10.3390/s23020639
Submission received: 1 December 2022 / Revised: 1 January 2023 / Accepted: 2 January 2023 / Published: 5 January 2023

Abstract

IoT environments are becoming increasingly heterogeneous in terms of their distributions and included entities by collaboratively involving not only data centers known from Cloud computing but also the different types of third-party entities that can provide computing resources. To transparently provide such resources and facilitate trust between the involved entities, it is necessary to develop and implement smart contracts. However, when developing smart contracts, developers face many challenges and concerns, such as security, contracts’ correctness, a lack of documentation and/or design patterns, and others. To address this problem, we propose a new recommender system to facilitate the development and implementation of low-cost EVM-enabled smart contracts. The recommender system’s algorithm provides the smart contract developer with smart contract templates that match their requirements and that are relevant to the typology of the fog architecture. It mainly relies on OpenZeppelin, a modular, reusable, and secure smart contract library that we use when classifying the smart contracts. The evaluation results indicate that by using our solution, the smart contracts’ development times are overall reduced. Moreover, such smart contracts are sustainable for fog-computing IoT environments and applications in low-cost EVM-based ledgers. The recommender system has been successfully implemented in the ONTOCHAIN ecosystem, thus presenting its applicability.
Keywords: smart contract; classification; cluster; recommender system; inheritance smart contract; classification; cluster; recommender system; inheritance

Share and Cite

MDPI and ACS Style

Gec, S.; Stankovski, V.; Lavbič, D.; Kochovski, P. A Recommender System for Robust Smart Contract Template Classification. Sensors 2023, 23, 639. https://doi.org/10.3390/s23020639

AMA Style

Gec S, Stankovski V, Lavbič D, Kochovski P. A Recommender System for Robust Smart Contract Template Classification. Sensors. 2023; 23(2):639. https://doi.org/10.3390/s23020639

Chicago/Turabian Style

Gec, Sandi, Vlado Stankovski, Dejan Lavbič, and Petar Kochovski. 2023. "A Recommender System for Robust Smart Contract Template Classification" Sensors 23, no. 2: 639. https://doi.org/10.3390/s23020639

APA Style

Gec, S., Stankovski, V., Lavbič, D., & Kochovski, P. (2023). A Recommender System for Robust Smart Contract Template Classification. Sensors, 23(2), 639. https://doi.org/10.3390/s23020639

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