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The paper introduces GPTScan to detect logic bugs in smart contracts. GPTScan combines LLM and traditional static analysis tools to create a new detection tool.

GPTScan depends little on the LLM, which only serves as a role of determining whether the target function has a bug or not. What’s more, the criteria for determining the bug is hand-written. So, only a small part of the tool is composed of LLM.

GPTScan achieves high precision (over 90%) for token contracts and acceptable precision (57.14%) for large projects, as well as a recall of over 70% for detecting ground-truth logic vulnerabilities.

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This paper demonstrates a new approach to replaying the Android bugs. More specifically, creates a new tool called AdbGPT to automatedly convert bug reports to reproduction. For the result, AdbGPT is able to reproduce 81.3% of bug reports in 253.6 seconds, outperforming the state-of-the-art baselines and ablation studies.

Background:

Bug reports often go on to contain the steps to reproduce (S2Rs) the bugs that assist developers to replicate and rectify the bugs, albeit with considerable amounts of engineering effort.