Smart contracts with excessive gas consumption can cause economic losses, such as black hole contracts. Acutal gas consumption depends on runtime information and has a probability distribution under different runtime situations. However, existing static analysis tools (e.g., Solc) cannot define runtime information and only provide an approximate upper bound on gas consumption without explanation. To address the challenge, we propose a label named GCL, which describes the probability distribution of Gas consumption, a code representation method containing domain features and a Graph Neural Network named Attention-based Graph Isomorphism Network (AGIN) oriented to domain feature, and SubgraphGas, a domain-oriented subgraph-level GNN explanation model. By combining AGIN and SubgraphGas, we have created a new explainable gas consumption prediction model (EGE). Our evaluations show that EGE outperforms prediction schemes based on Bi-LSTM. And EGE has similar explainability accuracy to general methods, but it is more efficient.

Foundation models, such as GPT-4, DALL-E have brought unprecedented AI “operating system” effect and new forms of human-AI interaction, sparking a wave of innovation in AI-native services, where natural language prompts serve as executable “code” directly (prompt as executable code), eliminating the need for programming language as an intermediary and opening up the door to personal AI. Prompt Sapper has emerged in response, committed to support the development of AI-native services by AI chain engineering. It creates a large language model (LLM) empowered software engineering infrastructure for authoring AI chains through human-AI collaborative intelligence, unleashing the AI innovation potential of every individual, and forging a future where everyone can be a master of AI innovation. This article will introduce the R\&D motivation behind Prompt Sapper, along with its corresponding AI chain engineering methodology and technical practices.