Smart Contract Generation Process

• Smart Contract Generation Process is a method for translating high-level functional and temporal specifications into executable blockchain code.
• It employs automated synthesis techniques like finite state machines and LTL-based property verification to ensure both safety and liveness properties.
• Case studies in crowdfunding, auctions, and licensing showcase the practical application of formal methods to produce verifiably correct smart contracts.

Smart contracts are blockchain-resident, stateful programs whose enforcement of contractual clauses is embedded in a decentralized, immutable platform. The smart contract generation process encompasses methodologies for converting high-level functional specifications—often formalized as temporal logic, state machines, or business process models—into executable, verifiably correct code that encodes the required state transitions and behaviors. Modern approaches focus on ensuring correctness by construction, leveraging program synthesis, formal verification, and model-driven engineering to mitigate irreparable security or semantic errors prior to deployment.

1. Formal Specification and State Machine Synthesis

The synthesis of smart contracts as described employs a model in which the contract is cast as a finite state machine (FSM), a formalism that is well-suited to capturing the stateful, event-driven nature of blockchain programs (Suvorov et al., 2019). The contract specification begins with a set of temporal logic formulas—most notably in linear temporal logic (LTL)—together with a suite of exemplary input/output sequences termed test scenarios. The logic formulas express critical properties, such as single-invocation constraints (e.g., "getFunds can only be called once") or necessary event orderings (e.g., "donate cannot follow getFunds"), typically using globally, next, until, and release temporal operators.

The FSM synthesis problem is formalized as generating a tuple (S, s_init, E, Z, δ, λ), where S are the states, s_init the initial state, E the set of input events (method invocations), Z the output actions (method bodies), δ the transition function, and λ the function mapping transitions to output actions. The synthesis incorporates LTL constraints algorithmically by encoding them (together with traces arising from test scenarios) into SAT or QSAT queries. Counterexamples—traces in which a candidate automaton violates an LTL constraint—are iteratively integrated until the FSM is consistent with both temporal properties and scenario behaviors. The resulting FSM can then be directly or indirectly translated into executable code, such as Solidity for Ethereum-like blockchains.

2. Linear Temporal Logic (LTL) in Contract Design

Central to the approach is the use of LTL to formally specify dynamic, temporal properties of smart contracts. LTL supports explicit, machine-verifiable encoding of safety (e.g., prevention of double-withdrawals), liveness (e.g., eventual contract closure), and ordering constraints (e.g., donation must precede withdrawal). Key formulas exemplified in the paper include:

• $$G\,(\texttt{getFunds} \to X\, \neg F\, \texttt{getFunds})$$ (enforces single-use of getFunds)
• $$G\,(\texttt{getFunds} \to \neg F\, \texttt{donate})$$ (forbids donations after funds withdrawal)

Specifying these properties in LTL gives rise to automata whose correctness can be established automatically, and whose transitions map bijectively to externally accessible contract methods. LTL specifications are further supported by concrete test scenarios—input sequences that reflect anticipated user interactions or adversarial behaviors.

The paper identifies the challenge that LTL expressiveness may be insufficient for certain classes of repetitious or infinite behaviors (e.g., "$\varphi$ occurs infinitely often while $\psi$ does not"). As a workaround, repeating patterns are instantiated with a sufficiently large, finite upper bound in the test scenarios to emulate the intended infinite behaviors.

3. Case Studies: Crowdfunding, Auction, and License Contracts

To demonstrate the methodology, the paper explores three archetypal smart contract scenarios:

a. Crowdfunding Platform

• Events: donate, getFunds, reclaim.
• Logic: Donation permitted within a designated window; getFunds became available once the campaign ended; reclaim allowed only if the campaign is unfunded.
• LTL: Strictly enforces single usage and sequencing policies, for example $$G(\texttt{getFunds} \to X\, \neg F\, \texttt{getFunds})$$.

b. Blinded Auction

• Events: bid, close, reveal, finish, cancel, unbid.
• Logic: bid until "biddingOver", then reveal and, eventually, either finish for winner payout or cancel for refund.
• LTL: Specifies that bids only occur pre-close, reveals post-close, finish/cancel govern closure, and refunds can be claimed only in appropriate states.

c. License Contract

• Parties: Licensor, Licensee.
• Events: getLicense, getApproval, use, publish, comment, remove, getCommission, terminate, with additional domains for detailed enforcement (e.g., event noRemove).
• Logic: Enforces that publish is contingent on approval, and triggers contract termination upon violation.
• LTL: Divides logic into general principles (e.g., uniqueness constraints) and contractual clauses (e.g., temporal dependencies between events).

In each, the formal LTL specification is coupled with representative scenarios and synthesized into an FSM, yielding Solidity code that implements and enforces the required temporal and logical constraints.

4. Formal Verification and Correctness Assurance

The methodology employs model checking at multiple stages to ensure implementation faithfulness to specification. Since deployed smart contracts are immutable, correctness before deployment is essential. The FSM—derived from the union of LTL constraints and behavioral test scenarios—is exhaustively checked for compliance with both safety and liveness properties. This process ensures that the Solidity code, being a direct or straightforward translation of the FSM, preserves these properties and reduces costly post-deployment vulnerabilities.

Verification is carried out both at the level of the abstract FSM (by asserting invariant properties or trace absence) and the generated code (by ensuring correspondences between method accessibility and state transitions).

5. Implementation Techniques and Synthesis Tools

The contract synthesis process relies on automated tools (e.g., EFSM-tools) that operationalize the translation from temporal logic and scenario-based specification to FSM representations. The FSM is typically rendered as an explicit state graph (diagrams in the paper visually reinforce this) whose edges have guards and whose nodes encapsulate contract states. Code generators map these FSMs to Solidity code, using patterns (such as a state variable and guarded function bodies) that conform to recommended Solidity design practices and are amenable to on-chain verification.

Table: FSM Synthesis Workflow

Step	Formalism	Output
Specification	LTL formulas, test scenarios	Temporal properties
FSM Synthesis	Tuple $(S, s_{\text{init}}, E, Z, \delta, \lambda)$	FSM structure
Verification/Model Checking	LTL model checker	Proof/violations
Code Generation	FSM-to-Solidity template	Executable contract

This structured workflow takes high-level design intent and produces code with formal guarantees about temporal and safety-critical behaviors.

6. Challenges and Limitations

Expressivity limitations in LTL (especially for properties requiring infinite behaviors or parameterized event counts) necessitate case-specific scenario modeling as a partial substitute. The methodology's effectiveness is demonstrated primarily for small and medium complexity contracts; scalability to very large or highly dynamic contracts remains an open question. Additionally, practical concerns such as gas efficiency or the full modeling of currency transfers are not intrinsically handled in the FSM synthesis and may require augmentation with other formal frameworks (e.g., SAT, SMT encodings as noted in the discussion).

The question of whether all contract logic necessitates a Turing-complete language is not settled, as many correct-by-construction FSM-synthesized contracts are, by nature, sub-Turing.

7. Implications and Future Directions

FSM-based smart contract generation provides a robust pathway for building secure, auditable blockchain systems by aligning code behavior strictly with formalized intent. This methodology reduces detection and mitigation of vulnerabilities to a pre-deployment activity, and supports confidence in code that will manage irreversible, high-stakes financial transactions. The explicit modeling of contract state and transitions exposes the underlying business logic for human and machine auditing.

Potential extensions include integrating economic properties (e.g., incentive compatibility), supporting compositional synthesis for interacting contracts, and extending temporal logic expressiveness for richer behavioral specification. The approach establishes a rigorous bridge between formal methods and practical smart contract engineering, elevating the standards for correctness and reliability in blockchain applications (Suvorov et al., 2019).