Smart contract security is essential for blockchain applications. While studies indicate that few reported vulnerabilities are exploited, no follow-up study has been performed to delve into the reasons behind this phenomenon. We aim to understand the reasons for the low exploitation rate to enhance vulnerability detection practices. We first collect 136,969 real-world smart contracts and analyze them using seven vulnerability detectors. Then, we apply Strauss’ grounded theory to gain insights into exploitability. Furthermore, we analyze transaction logs to identify exploitations in history.  Among the 4,364 smart contracts reported as vulnerable by the vulnerability detectors, a noteworthy 75.25% were found to be unexploitable. Merely 66 (0.015%) exploitable contracts have been exploited. We uncover 11 reasons leading to reporting unexploitable vulnerabilities. Additionally, we identified five factors that could potentially reduce the likelihood of exploitable contracts being targeted. Our findings emphasize the importance of not merely treating smart contracts as conventional object-oriented (OO) applications. Researchers need to account for the unique aspects of the Solidity language, the design principles underpinning smart contracts, and specific execution environments. This nuanced approach is essential for minimizing the occurrence of reporting unexploitable vulnerabilities. Based on the study's insights, we propose six recommendations to enhance smart contract vulnerability detection, prioritization, and mitigation.  

Jupyter Notebook is a popular tool among data analysts and scientists for working with data. It provides a way to combine code, documentation, and visualizations in a single, interactive environment, facilitating code reuse. While code reuse can improve programming efficiency, it can also decrease readability, security, and overall performance. We conduct a large-scale exploratory study of code reuse practices in the Jupyter Notebook development community on the Stack Overflow platform to understand the potential negative impacts of code reuse. Our findings identified 1,097,470 Jupyter Notebook clone pairs that reuse Stack Overflow code snippets, and the average code snippet has 7.91 code quality violations. Through our research, we gain insight into the reasons behind Jupyter Notebook developers' decision to reuse code and the potential drawbacks of this practice.