Verified Smart Contract Code Comments Benchmark Code Generation The current state of the art on verified smart contract code comments is gpt j 6b smart contract. see a full comparison of 2 papers with code. Since code clones are common in smart contract development, ccgir finds the most similar code in the code repository and reuses its comment through an information retrieval approach from three aspects: semantic similarity, lexical similarity, and syntactic similarity of smart contract codes.

Github Bit Smartcontract Analysis Smartcontract Benchmark These In this paper, we propose a novel retrival enhanced approach ccgra (code comment generation with retrival enhanced approach). our approach leverages the advantages of pre trained language models. In this study, we propose an approach sccllm based on llms and in context learning. specifically, in the demonstration selection phase, sccllm retrieves the top k code snippets from the historical corpus by considering syntax, semantics, and lexical information. About ccgra: smart contract code comment generation with retrieval enhanced approach. Verified smart contracts code comments is a dataset of real ethereum smart contract functions, containing "code, comment" pairs of both solidity and vyper source code.

Automation Approaches For Smart Contract Code Generation Download About ccgra: smart contract code comment generation with retrieval enhanced approach. Verified smart contracts code comments is a dataset of real ethereum smart contract functions, containing "code, comment" pairs of both solidity and vyper source code. In this article, we describe the process of generating smart contract code using ai driven tools and its subsequent verification via the insertion modeling system. In this study, we propose an approach sccllm based on llms and in context learning. specifically, in the demonstration selection phase, sccllm retrieves the top k code snippets from the historical corpus by considering syntax, semantics, and lexical information. We introduce clever, a high quality, curated benchmark of 161 problems for end to end verified code generation in lean. each problem consists of (1) the task of generating a specification that matches a held out ground truth specification, and (2) the task of generating a lean implementation that provably satisfies this specification. We propose a novel vulnerability constrained decoding approach to reduce the amount of vulnerable code generated by such models. using a small dataset of labeled vulnerable lines of code, we fine tune an llm to include vulnerability labels when generating code, acting as an embedded classifier.