TransRacer: Function Dependence-Guided Transaction Race Detection for Smart Contracts
Chenyang Ma, Wei Song, Jeff Huang
Abstract
Smart contracts are programs that define rules for transactions running on blockchains. Since any qualified transaction sequence within the same block can be orchestrated by the blockchain miner, unexpected results may occur due to data races between transactions (called transaction races). Surprisingly, transaction races in smart contracts have not been fully investigated. To address this, we propose TransRacer, an automated approach and open-source tool that employs symbolic execution to detect transaction races in smart contracts. TransRacer analyzes function dependencies to identify transaction races hidden in specific contract states. It also generates witness transactions that can trigger such races. The experimental results on 50 real-world smart contracts show the effectiveness and efficiency of TransRacer: it detects 426 races in 255.9 minutes, including 149 race bugs leading to inconsistent states.
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