Machine Learning Based Bug Prediction Engine For Smart Contracts

Abstract

AbstractAs blockchain solutions become widespread, identifying potential bugs in smart contracts written in Solidity language will be important for these solutions to work correctly. To accurately detect these bugs, the developer must use several state-of-the-art bug detection tools and investigate the potential bugs they report. In this study, we first show that one tool is not enough to detect all the bugs as our Static Analysis for Solidity tool (SA-Solidity) and the known SmartCheck and Securify tools identify different bugs in SmartEmbed’s experimental set of smart contracts. Then, we develop Machine Learning-based Bug Predictor for Solidity (MLBP-Solidity) which predicts files that would be reported by all the previous bug detection tools. MLBP-Solidity eases the burden on the developer by allowing him/her to focus on a subset of files that are most probably buggy. Our experimental results show that MLBP-Solidity achieves 91-99% accuracy, depending on the type of predicted bug.