Chord: Towards a Unified Detection of Blockchain Transaction Parallelism Bugs

Yuanhang Zhou; Zhen Yan; Yuanliang Chen; Fuchen Ma; Ting Chen; Yu Jiang

doi:10.1109/icse55347.2025.00205

ICSE2025Top-tier venue

Chord: Towards a Unified Detection of Blockchain Transaction Parallelism Bugs

Yuanhang Zhou, Zhen Yan, Yuanliang Chen, Fuchen Ma, Ting Chen, Yu Jiang

DOI Publisher

2025Year

1Citations

1Top-tier citations

Abstract

Blockchain systems have implemented various transaction parallelism mechanisms to improve the system throughput and reduce the latency. However, they inevitably introduce bugs. Such bugs can result in severe consequences such as asset loss, double spending, consensus failure, and DDoS. Unfortunately, they have been little analyzed about their symptoms and root causes, leading to a lack of effective detection methods.

In this work, we conduct a thorough analysis of historical transaction parallelism bugs in four commercial blockchains. Results show that most of them arise from mishandling conflicting transactions and manifest without obvious phenomena. However, given the heterogeneity of blockchains, it is challenging to trigger conflict handling in a unified way. Effectively identifying these bugs is also hard. Inspired by the findings, we propose Chord, aiming at detecting blockchain transaction parallelism bugs. Chord proposes a unified conflict transaction model to generate various conflict transactions. Chord also dynamically adjust the transaction submission and inserts proactive reverts during transaction execution to conduct thorough testing. Besides, Chord incorporates a local-remote differential oracle and a TPS oracle to capture the bugs. Our evaluation shows that Chord successfully detects 54 transaction parallelism bugs. Besides, Chord outperforms the existing methods by decreasing the TPS by 49.7% and increasing the latency by 388.0%, showing its effectiveness in triggering various conflict scenarios and exposing the bugs.

Yuanhang Zhou and Zhen Yan contributed equally to this work.

Fuchen Ma and Yu Jiang are the corresponding authors.

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