Semantic Code Search for Smart Contracts

Abstract

Semantic code search technology allows searching for existing code snippets through natural language, which can greatly improve programming efficiency. Smart contracts, programs that run on the blockchain, have a code reuse rate of more than 90%, which means developers have a great demand for semantic code search tools. However, the existing code search models still have a semantic gap between code and query, and perform poorly on specialized queries of smart contracts. In this paper, we propose a Multi-Modal Smart contract Code Search (MM-SCS) model. Specifically, we construct a Contract Elements Dependency Graph (CEDG) for MM-SCS as an additional modality to capture the data-flow and control-flow information of the code. To make the model more focused on the key contextual information, we use a multi-head attention network to generate embeddings for code features. In addition, we use a finetuned pretrained model to ensure the model's effectiveness when the training data is small. We compared MM-SCS with four stateof-the-art models on a dataset with 470K (code, docstring) pairs collected from Github and Etherscan. Experimental results show that MM-SCS achieves an MRR (Mean Reciprocal Rank) of 0.572, outperforming four state of-the-art models UNIF, DeepCS, CARLCS-CNN, and TAB-CS by 34.2%, 59.3%, 36.8%, and 14.1%, respectively. Additionally, the search speed of MM-SCS is second only to UNIF, reaching 0.34s/query. CCS CONCEPTS• Software and its engineering → Search-based software engineering.

Towards Accurate Knowledge Transfer between Transformer-based Models for Code Summarization

“…We randomly selected 450K Python <code, comment> pairs from the widely used corpus CodeSearchNet [4], and 45K Solidity pairs from the corpus used in [6] to build our dataset. We split original code texts by a set of symbols, i.e., { .…”