Malo in the Code Jungle: Explainable Fault Localization for Decentralized Applications

Hui Zhang, Jiajing Wu, Zhiying Wu, Zhe Chen, Dan Lin, Jiachi Chen, Yuren Zhou, Zibin Zheng

Abstract: Decentralized applications (DApps) have long been sitting ducks for hackers due to their valuable cryptocurrency assets, exposing them to various security risks. When a DApp is attacked, promptly identifying faults is crucial to minimizing financial losses and ensuring effective fault repair. However, existing fault localization methods, which mostly rely on code coverage, often fall short for DApps, particularly when dealing with only one fault case. Furthermore, according to a prior survey, most developers expect fault localization tools to provide reasonable explanations. In this paper, we present Malo, a method for DApp-specific explainable fault localization. It identifies fault functions through suspicious token transfer-guided analysis, and then employs Large Language Models (LLMs) to generate explanations for these identified fault functions. Specifically, Malo examines function call traces and source codes of fault cases to acquire internal knowledge, and also retrieves relevant project documents from the Web to obtain external knowledge. By integrating internal and external knowledge, Malo generates reasonable explanations for faults in DApps. Our evaluation on a dataset of 68 real-world DApp faults demonstrates that Malo can locate 62% of faults within the Top-5, 9% higher than the state-of-the-art method. The experiment results also demonstrate a remarkable alignment accuracy of 71% between the explanations generated by Malo and the ground truth. In addition, we conduct a user study, which confirms that explanations generated by Malo can aid developers in comprehending the root cause of faults. Our code and dataset are available online: https://github.com/SodalimeZero/Malo_Code.git.

External IDs:doi:10.1109/tse.2025.3578816