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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.13808 |
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| _version_ | 1866912904502050816 |
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| author | Goel, Abhinav Shah, Chaitya Capponi, Agostino Gliozzo, Alfio |
| author_facet | Goel, Abhinav Shah, Chaitya Capponi, Agostino Gliozzo, Alfio |
| contents | We present an end-to-end framework for systematic evaluation of LLM-generated smart contracts from natural-language specifications. The system parses contractual text into structured schemas, generates Solidity code, and performs automated quality assessment through compilation and security checks. Using CrewAI-style agent teams with iterative refinement, the pipeline produces structured artifacts with full provenance metadata. Quality is measured across five dimensions, including functional completeness, variable fidelity, state-machine correctness, business-logic fidelity, and code quality aggregated into composite scores. The framework supports paired evaluation against ground-truth implementations, quantifying alignment and identifying systematic error modes such as logic omissions and state transition inconsistencies. This provides a reproducible benchmark for empirical research on smart contract synthesis quality and supports extensions to formal verification and compliance checking. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_13808 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | An end-to-end agentic pipeline for smart contract translation and quality evaluation Goel, Abhinav Shah, Chaitya Capponi, Agostino Gliozzo, Alfio Artificial Intelligence Software Engineering D.3; I.2; J.6; D.2 We present an end-to-end framework for systematic evaluation of LLM-generated smart contracts from natural-language specifications. The system parses contractual text into structured schemas, generates Solidity code, and performs automated quality assessment through compilation and security checks. Using CrewAI-style agent teams with iterative refinement, the pipeline produces structured artifacts with full provenance metadata. Quality is measured across five dimensions, including functional completeness, variable fidelity, state-machine correctness, business-logic fidelity, and code quality aggregated into composite scores. The framework supports paired evaluation against ground-truth implementations, quantifying alignment and identifying systematic error modes such as logic omissions and state transition inconsistencies. This provides a reproducible benchmark for empirical research on smart contract synthesis quality and supports extensions to formal verification and compliance checking. |
| title | An end-to-end agentic pipeline for smart contract translation and quality evaluation |
| topic | Artificial Intelligence Software Engineering D.3; I.2; J.6; D.2 |
| url | https://arxiv.org/abs/2602.13808 |