GuessGas:Tell me Fine-Grained Gas Consumption of My Smart Contract and Why
In this paper, we present the development of an explainable Gas Estimator model EGE, utilizing big data to mine potential distribution of runtime information. Our approach overcomes the limitations of static gas analysis tools by labeling functions based on interval probability distribution and building code representations containing program semantics. We also considered domain features during training to improve prediction accuracy. Our domain-oriented subgraph-level GNN explanation model SubgraphGas provides explanations for predictions.
Email Address of Submitting Authorcrx934080895@gmail.com
Submitting Author's InstitutionJiangxi Normal University
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