@inproceedings{Liu2025b,
          author    = {C. G. Liu and P. Bodorik and D. Jutla},
          title     = {Generation of Smart Contracts by Business Analyst with TABS+R Tool [Manuscript accepted for publication]},
          booktitle = {45th IEEE International Conference on Distributed Computing Systems Workshops (ICDCSW 2025)},
          year      = {2025},
          abstract  = {This paper addresses the challenge of generating smart contracts from Business Process Management and Notation (BPMN) models, a key area of blockchain research. In our prior work, we introduced TABS, a methodology and tool that automates the generation of smart contracts from BPMN models. This approach abstracts the BPMN flow control, making it independent of the underlying blockchain infrastructure, with only the BPMN task elements requiring coding. In subsequent research, we enhanced our approach by adding support for nested transactions and enabling smart contract repair and upgrades. To empower Business Analysts (BAs) to generate smart contracts without relying on software developers, we incorporated Decision Management Notation (DMN) to represent business rules that define BPMN task functionalities. DMN allows for simple decision-making through the FEEL language and supports references to fields in semi-structured documents flowing through the process. This paper demonstrates how BAs, even without software development expertise, can use our TABS+R tool to automatically generate and deploy smart contracts on blockchain platforms.},
          url       = {https://blockchain.cs.dal.ca/papers/7c-ICDCS-2025.pdf}
        }

        

This paper addresses the challenge of generating smart contracts from Business Process Management and Notation (BPMN) models, a key area of blockchain research. In our prior work, we introduced TABS, a methodology and tool that automates the generation of smart contracts from BPMN models. This approach abstracts the BPMN flow control, making it independent of the underlying blockchain infrastructure, with only the BPMN task elements requiring coding. In subsequent research, we enhanced our approach by adding support for nested transactions and enabling smart contract repair and upgrades. To empower Business Analysts (BAs) to generate smart contracts without relying on software developers, we incorporated Decision Management Notation (DMN) to represent business rules that define BPMN task functionalities. DMN allows for simple decision-making through the FEEL language and supports references to fields in semi-structured documents flowing through the process. This paper demonstrates how BAs, even without software development expertise, can use our TABS+R tool to automatically generate and deploy smart contracts on blockchain platforms.

@inproceedings{Liu2025a,
          author    = {C. G. Liu and P. Bodorik and D. Jutla},
          title     = {BPMN to Smart Contract by Business Analyst [Manuscript accepted for presentation]},
          booktitle = {International Workshop on Blockchain Applications and Theory (BCAT), 5th Intelligent Cybersecurity Conference (ICSC 2025)},
          year      = {2025},
          url       = {https://easychair.org/conferences/?conf=icsc20250}}

          

@inproceedings{liu2025c,
          title     = {Automated Mechanism to Support Trade Transactions in Smart Contracts with Upgrade and Repair},
          journal   = {Elsevier Journal of Blockchain: Research and Applications},
          booktitle = {Elsevier Journal of Blockchain: Research and Applications},
          year      = {2025},
          author    = {Liu, C. G. and Bodorik, P. and Jutla, D.},
          doi       = {https://doi.org/10.1016/j.bcra.2025.100285},
          keywords  = {Transactions, Blockchain, Smart Contracts, Trade of Goods and Services, Trade Transactions, Transactional Properties, Privacy, Transaction Mechanism, Recovery Procedures},
          abstract  = {In our previous research, we addressed the problem of automated transformation of models, represented using the Business Process Model and Notation (BPMN) standard, into the methods of a smart contract. The transformation supports BPMN models that contain complex multi-step activities that are supported using our concept of multi-step nested trade transactions, wherein the transactional properties are enforced by a mechanism generated automatically by the transformation process from a BPMN model to a smart contract. In this paper, we present a methodology for repairing a smart contract that cannot be completed due to events arising that were not anticipated by the developer and thus prevent the completion of the smart contract. The repair process starts with the original BPMN model fragment causing the issue, providing a modeler with the innermost transaction fragment containing the failed activity. The modeler amends the BPMN pattern based on successful completion of previous activities. If repairs exceed the inner transaction’s scope, they are addressed using the parent transaction's BPMN model. The amended BPMN model is then transformed into a new smart contract(s), ensuring consistent data and logic transitions. Previously, we developed TABS+ as a Proof of Concept to transform BPMN models into smart contracts for nested transactions. This paper describes the tool TABS+R, developed by extending the TABS+ tool, to allow repair of smart contracts.},
          url       = {https://doi.org/10.1016/j.bcra.2025.100285}
        }

In our previous research, we addressed the problem of automated transformation of models, represented using the Business Process Model and Notation (BPMN) standard, into the methods of a smart contract. The transformation supports BPMN models that contain complex multi-step activities that are supported using our concept of multi-step nested trade transactions, wherein the transactional properties are enforced by a mechanism generated automatically by the transformation process from a BPMN model to a smart contract. In this paper, we present a methodology for repairing a smart contract that cannot be completed due to events arising that were not anticipated by the developer and thus prevent the completion of the smart contract. The repair process starts with the original BPMN model fragment causing the issue, providing a modeler with the innermost transaction fragment containing the failed activity. The modeler amends the BPMN pattern based on successful completion of previous activities. If repairs exceed the inner transaction’s scope, they are addressed using the parent transaction's BPMN model. The amended BPMN model is then transformed into a new smart contract(s), ensuring consistent data and logic transitions. Previously, we developed TABS+ as a Proof of Concept to transform BPMN models into smart contracts for nested transactions. This paper describes the tool TABS+R, developed by extending the TABS+ tool, to allow repair of smart contracts.

@inproceedings{Liu2024SmartContracts,
          author    = {C. G. Liu and P. Bodorik and D. Jutla},
          title     = {Smart Contracts for SMEs and Large Companies},
          booktitle = {2024 IEEE Virtual Conference on Communications (IEEE VCC)},
          year      = {2024},
          doi       = {https://doi.org/10.1109/VCC63113.2024.10914418}
        }

        

@inproceedings{author2024c,
          title        = {Transforming Automatically BPMN Models to Smart Contracts with Nested Collaborative Transactions (TABS+)},
          journal      = {ACM Journal Distributed Ledger Technologies: Research and Practice},
          booktitle    = {ACM Journal Distributed Ledger Technologies: Research and Practice, Volume 3, Issue 3, Article No.: 21, Pages 1-37},
          year         = {2024},
          author       = {Liu, C. G. and Bodorik, P. and Jutla, D.},
          doi          = {https://doi.org/10.1145/3654802},
          url_paper    = {https://doi.org/10.1145/3654802},
          paper        = {https://doi.org/10.1145/3654802},
          url_download = {https://blockchain.cs.dal.ca/works/9%20ACM%20DLT%2023%20BPMNtoSC-ACM-DLT-submitted.pdf}
        }

        

@inproceedings{BODORIK2022100115,
          title        = {TABS: Transforming automatically BPMN models into blockchain smart contracts},
          journal      = {Elsevier Journal of Blockchain: Research and Applications},
          booktitle    = {Elsevier Journal of Blockchain: Research and Applications},
          volume       = {4, 100115},
          number       = {1},
          pages        = {1-26},
          year         = {2023},
          issn         = {2096-7209},
          doi          = {https://doi.org/10.1016/j.bcra.2022.100115},
          url_paper    = {https://www.sciencedirect.com/science/article/pii/S2096720922000562},
          url_download = {https://www.sciencedirect.com/science/article/pii/S2096720922000562},
          paper        = {https://www.sciencedirect.com/science/article/pii/S2096720922000562},
          author       = {Bodorik, P. and Liu, C. G. and B Jutla, D.},
          keywords     = {Blockchain, Business processes modeling notation (BPMN), Discrete event (DE) modeling, Finite state machine (FSM), Hierarchical state machines (HSM), Smart contract, Off-chain computation, Privacy, Smart contract interoperability},
          abstract     = {Research on blockchains addresses multiple issues, with one being automated creation of smart contracts. Developing smart contract methods is more difficult than mainstream software development as the underlying blockchain infrastructure poses additional complexity. We report on a new approach to develop smart contracts with the objective to automate the process to increase developer efficiency and reduce risks of errors introduced by software developers. To support industry adoption, we use Business Process Model and Notation (BPMN) modeling to describe an application while targeting applications in the trade vertical. We describe a system that transforms a BPMN model into a multi-modal model that combines Discrete Event (DE) modeling for concurrency with Hierarchical State Machines (HSMs) to represent application functionality. Then, further transformations are used to transform the DE-HSM model into methods in smart contracts. The system lets the modeler decide which of the independent patterns should be transformed into methods of a separate smart contract that is deployed on a sidechain for the purpose of (i) reducing processing costs and/or (ii) providing privacy so that other participants in the smart contract do not have visibility into processing of the pattern. We also briefly describe a proof-of-concept tool we built to demonstrate the feasibility of our approach.},
          note         = {Impact Factor: 5.6, CiteScore: 11.3.}
        }


        

Research on blockchains addresses multiple issues, with one being automated creation of smart contracts. Developing smart contract methods is more difficult than mainstream software development as the underlying blockchain infrastructure poses additional complexity. We report on a new approach to develop smart contracts with the objective to automate the process to increase developer efficiency and reduce risks of errors introduced by software developers. To support industry adoption, we use Business Process Model and Notation (BPMN) modeling to describe an application while targeting applications in the trade vertical. We describe a system that transforms a BPMN model into a multi-modal model that combines Discrete Event (DE) modeling for concurrency with Hierarchical State Machines (HSMs) to represent application functionality. Then, further transformations are used to transform the DE-HSM model into methods in smart contracts. The system lets the modeler decide which of the independent patterns should be transformed into methods of a separate smart contract that is deployed on a sidechain for the purpose of (i) reducing processing costs and/or (ii) providing privacy so that other participants in the smart contract do not have visibility into processing of the pattern. We also briefly describe a proof-of-concept tool we built to demonstrate the feasibility of our approach.

@inproceedings{liu2023longterm,
          title     = {Long-Term Blockchain Transactions Spanning Multiplicity of Smart Contract Methods},
          author    = {Liu, C. G. and Bodorik, P. and Jutla, D.},
          booktitle = {Springer BlockSys: Blockchain and Trustworthy Systems},
          year      = {2023},
          pages     = {141-155},
          doi       = {https://doi.org/10.1007/978-981-99-8104-5},
          isbn      = {978-981-99-8103-8},
          eisbn     = {978-981-99-8104-5}
        }

        

@inproceedings{9922193,
          author       = {Liu, Christian Gang and Bodorik, Peter and Jutla, Dawn},
          booktitle    = {2022 ACM Fourth International Conference on Blockchain Computing and Applications (BCCA)},
          title        = {Supporting Long-term Transactions in Smart Contracts},
          year         = {2022},
          pages        = {11-19},
          volume       = {},
          number       = {},
          doi          = {10.1109/BCCA55292.2022.9922193},
          url_paper    = {https://ieeexplore.ieee.org/document/9922193},
          url_download = {https://web.cs.dal.ca/~gang/BCCA/},
          paper        = {https://ieeexplore.ieee.org/document/9922193}
        }

        

@inproceedings{liu_automating_2021,
          title        = {Automating Smart Contract Generation on Blockchains Using Multi-modal Modeling},
          volume       = {13, No. 3},
          issn         = {1798-2340},
          url          = {http://www.jait.us/index.php?m=content&c=index&a=show&catid=217&id=1219},
          doi          = {10.12720/jait.13.3.213-223},
          abstract     = {The power and correctness of smart contracts have been the focus of much research. We propose a new approach for developing smart contracts that uses multi-modal modeling to represent the application logic for the trade domain. We use discrete events modeling for concurrency combined with {FSM} modeling to use concurrent {FSMs} to not only simplify the design process for the modeler, but also to scale the application running on a blockchain and facilitate identifying parts of a smart program that are suitable for off-chain processing on a sidechain that also provides privacy. In addition, we achieve separation of concerns between (a) application logic and (b) its transformation into a smart contract and deployment on a blockchain with processing of selected patterns on private sidechains. We transform the model into a smart contract automatically, such that patterns, selected by the modeler, are deployed on a sidechain. The interface for the mainchain to sidechain interaction is also prepared and deployed automatically.},
          pages        = {213--223},
          journaltitle = {10.12720/jait},
          shortjournal = {{JAIT} is a scientific open access journal which focuses on empirical research results and critical analysis of technology development, use, management and impacts in information technology.},
          booktitle    = {Journal of Advances in Information Technology (JAIT)},
          author       = {Liu, C. G. and Bodorik, P. and Jutla, D.},
          year         = {2022},
          note         = {Acceptance Rate: 5%, CiteScore: 4.2.}
        }



        

The power and correctness of smart contracts have been the focus of much research. We propose a new approach for developing smart contracts that uses multi-modal modeling to represent the application logic for the trade domain. We use discrete events modeling for concurrency combined with FSM modeling to use concurrent FSMs to not only simplify the design process for the modeler, but also to scale the application running on a blockchain and facilitate identifying parts of a smart program that are suitable for off-chain processing on a sidechain that also provides privacy. In addition, we achieve separation of concerns between (a) application logic and (b) its transformation into a smart contract and deployment on a blockchain with processing of selected patterns on private sidechains. We transform the model into a smart contract automatically, such that patterns, selected by the modeler, are deployed on a sidechain. The interface for the mainchain to sidechain interaction is also prepared and deployed automatically.

@inproceedings{10.1145/3460537.3460565,
          author    = {Bodorik, Peter and G. Liu, Christian and Julta, Dawn},
          title     = {Using FSMs to Find Patterns for Off-Chain Computing: Finding Patterns for Off-Chain Computing with FSMs},
          year      = {2021},
          isbn      = {9781450389624},
          address   = {New York, NY, USA},
          url       = {https://doi.org/10.1145/3460537.3460565},
          doi       = {10.1145/3460537.3460565},
          abstract  = {One of the problems arising in using blockchains is their size-constraints regarding performance. This paper proposes a new algorithm for blockchain software developers and architects to use for determining what computations of a smart contract can be effectively done off-chain without loss of trust. Our algorithm uses FSMs or HSMs in order to create smart contract patterns using graphs and then uses pattern recognition to identify which parts of the smart contracts should be considered for moving off-chain. The pattern recognition property used is that once software execution transits into the pattern's entry state, off-chain execution continues until the execution transits into the pattern's exit state, when execution continues on-chain. The software developer reviews each pattern together with information, such as anticipated overhead cost due to off-chain execution, and she is provided with guidance on decision making whether to execute the smart contract pattern under consideration off-chain. Expert software developer inspection, in the context of a Trade Finance use case, validates that our algorithm finds optimal patterns for moving computations off-chain and improve blockchain software performance.},
          booktitle = {2021 The 3rd ACM International Conference on Blockchain Technology},
          pages     = {28–34},
          numpages  = {7},
          keywords  = {FSM and HSM modeling, Blockchain developer productivity, Smart contract, Off-chain computation, Blockchain},
          location  = {Shanghai, China},
          series    = {ICBCT '21}
        }



        

One of the problems arising in using blockchains is their size-constraints regarding performance. This paper proposes a new algorithm for blockchain software developers and architects to use for determining what computations of a smart contract can be effectively done off-chain without loss of trust. Our algorithm uses FSMs or HSMs in order to create smart contract patterns using graphs and then uses pattern recognition to identify which parts of the smart contracts should be considered for moving off-chain. The pattern recognition property used is that once software execution transits into the pattern's entry state, off-chain execution continues until the execution transits into the pattern's exit state, when execution continues on-chain. The software developer reviews each pattern together with information, such as anticipated overhead cost due to off-chain execution, and she is provided with guidance on decision making whether to execute the smart contract pattern under consideration off-chain. Expert software developer inspection, in the context of a Trade Finance use case, validates that our algorithm finds optimal patterns for moving computations off-chain and improve blockchain software performance.

@inproceedings{9659771,
          author       = {Liu, Chris and Bodorik, Peter and Jutla, Dawn},
          booktitle    = {2021 IEEE International Conference on Engineering and Emerging Technologies (ICEET)},
          title        = {From BPMN to smart contracts on blockchains: Transforming BPMN to DE-HSM multi-modal model},
          year         = {2021},
          volume       = {},
          number       = {},
          pages        = {1-7},
          abstract     = {Scalability, privacy, and interoperability are some of the major issues receiving attention in research on blockchain technologies. We concentrate on the trade finance vertical for which we develop a new modeling approach with the objective of automatic transformation of an application, represented using Business Process Model and Notation (BPMN), into a smart contract deployed on a blockchain. Here, we describe how the BPMN model is transformed into a multimodal model that combines DE-HSM modeling. We provide a high-level overview of the method and review how BPMN categories of elements are transformed into a multi-modal DE-HSM model. We also describe briefly how the DE-FMS model is automatically transformed into deployable smart contracts that interact to form a distributed application: The smart contract deployed on the main blockchain coordinates activities amongst the business partners and interoperates with smart contracts, also automatically prepared and deployed on a sidechain(s), with one smart contract per individual business partner. Privacy is obtained by performing activities, which are not germane to the collaboration with the other business partners but deal with the private activities of the individual business partner, in a smart contract deployed and executed on a side chain. We thus provide for interoperability of smart contracts and privacy as private activities of a business partner are performed in a smart contract on a private sidechain.},
          keywords     = {},
          doi          = {10.1109/ICEET53442.2021.9659771},
          issn         = {2409-2983},
          month        = {Oct},
          url_paper    = {https://ieeexplore.ieee.org/document/9659771},
          url_download = {https://web.cs.dal.ca/~gang/ICEET/},
          paper        = {https://ieeexplore.ieee.org/document/9659771}
        }

        

Scalability, privacy, and interoperability are some of the major issues receiving attention in research on blockchain technologies. We concentrate on the trade finance vertical for which we develop a new modeling approach with the objective of automatic transformation of an application, represented using Business Process Model and Notation (BPMN), into a smart contract deployed on a blockchain. Here, we describe how the BPMN model is transformed into a multimodal model that combines DE-HSM modeling. We provide a high-level overview of the method and review how BPMN categories of elements are transformed into a multi-modal DE-HSM model. We also describe briefly how the DE-FMS model is automatically transformed into deployable smart contracts that interact to form a distributed application: The smart contract deployed on the main blockchain coordinates activities amongst the business partners and interoperates with smart contracts, also automatically prepared and deployed on a sidechain(s), with one smart contract per individual business partner. Privacy is obtained by performing activities, which are not germane to the collaboration with the other business partners but deal with the private activities of the individual business partner, in a smart contract deployed and executed on a side chain. We thus provide for interoperability of smart contracts and privacy as private activities of a business partner are performed in a smart contract on a private sidechain.

@inproceedings{10.1145/3457337.3457848,
          author    = {Liu, Christian and Bodorik, Peter and Jutla, Dawn},
          title     = {A Tool for Moving Blockchain Computations Off-Chain},
          year      = {2021},
          isbn      = {9781450384001},
          address   = {New York, NY, USA},
          url       = {https://doi.org/10.1145/3457337.3457848},
          doi       = {10.1145/3457337.3457848},
          abstract  = {This paper proposes a new approach and tool for blockchain software developers and architects to use for determining which computations of a smart contract can be effectively done off-chain without loss of trust and how they can be moved off-chain automatically. Our approach uses Finite State Machines (FSMs) and Hierarchical State Machines (HSMs) modeling in order to create smart contract patterns using graphs and then use pattern properties to identify which parts of the smart contracts should be considered for moving off-chain. Once software execution transits into the pattern's entry state, off-chain execution continues until the execution transits into the pattern's exit state, when execution continues on-chain. This paper further describes a tool for (1) FSM input and visualization, its analysis, and automatic transformation into a smart contract, and (2) an interface to support the developers' decision to optimally off-chain computations and to (3) define the communications between on-chain and off-chain computations.},
          booktitle = {Proceedings of the 3rd ACM International Symposium on Blockchain and Secure Critical Infrastructure},
          pages     = {103–109},
          numpages  = {7},
          keywords  = {hierarchical state machine, blockchain, HSM, simple-graph, off-chain computation, transformation of FSM to smart contract, smart contract, FSM model},
          location  = {Virtual Event, Hong Kong},
          series    = {BSCI '21}
        }

        

This paper proposes a new approach and tool for blockchain software developers and architects to use for determining which computations of a smart contract can be effectively done off-chain without loss of trust and how they can be moved off-chain automatically. Our approach uses Finite State Machines (FSMs) and Hierarchical State Machines (HSMs) modeling in order to create smart contract patterns using graphs and then use pattern properties to identify which parts of the smart contracts should be considered for moving off-chain. Once software execution transits into the pattern's entry state, off-chain execution continues until the execution transits into the pattern's exit state, when execution continues on-chain. This paper further describes a tool for (1) FSM input and visualization, its analysis, and automatic transformation into a smart contract, and (2) an interface to support the developers' decision to optimally off-chain computations and to (3) define the communications between on-chain and off-chain computations.