Prediction of defects in Smart Contracts applying Deep Learning with Solidity metrics

Abstract

Smart Contracts are autonomous, self-executable programs that facilitate agreement execution without the need for intermediaries. These contracts are also susceptible to software defects, leading to vulnerabilities that can be exploited by attackers. The use of models for predicting software defects is a well-studied research area. However, applying these models with Smart Contract metrics is an area that remains underexplored. The aim of this study is to evaluate whether deep learning models used in the prediction of traditional software defects produce equivalent results with specific Smart Contract metrics. Machine learning models were applied to four data sets, and performances were evaluated using Precision, Recall, F-score, Area under the curve (AUC), Precision-recall curve (PRC), and Matthews Correlation Coefficient (MCC). This approach complements traditional formal verification methods, which, although accurate, are often slower and less adaptable to emerging vulnerabilities. By employing deep learning, the model enables faster and more cost-effective analysis of large volumes of Smart Contracts. Unlike conventional techniques that rely on expert-defined rules and require substantial computational resources, this model offers scalable and continuous monitoring. Consequently, the research provides a complementary solution that can significantly enhance the security of the smart contract ecosystem, allowing for the detection of potential defects before exploitation occurs.