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Coverage-guided fuzzing has become mainstream in fuzzing to automatically expose program vulnerabilities. Recently, a group of fuzzers are proposed to adopt a random search mechanism namely Havoc, explicitly or implicitly, to augment their edge exploration. However, they only tend to adopt the default setup of Havoc as an implementation option while none of them attempts to explore its power under diverse setups or inspect its rationale for potential improvement. In this paper, to address such issues, we conduct the first empirical study on Havoc to enhance the understanding of its characteristics. Specifically, we first find that applying the default setup of Havoc to fuzzers can significantly improve their edge coverage performance. Interestingly, we further observe that even simply executing Havoc itself without appending it to any fuzzer can lead to strong edge coverage performance and outperform most of our studied fuzzers. Moreover, we also extend the execution time of Havoc and find that most fuzzers can not only achieve significantly higher edge coverage, but also tend to perform similarly (i.e., their performance gaps get largely bridged). Inspired by the findings, we further propose Havoc𝑀𝐴𝐵, which models the Havoc mutation strategy as a multi-armed bandit problem to be solved by dynamically adjusting the mutation strategy. The evaluation result presents that Havoc𝑀𝐴𝐵 can significantly increase the edge coverage by 11.1% on average for all the benchmark projects compared with Havoc and even slightly outperform state-of-the-art QSYM which augments its computing resource by adopting three parallel threads. We further execute Havoc𝑀𝐴𝐵 with three parallel threads and result in 9% higher average edge coverage over QSYM upon all the benchmark projects 

Objective: The rapid growth of online health social websites has captured a vast amount of healthcare information and made the information easy to access for health consumers. E-patients often use these social websites for informational and emotional support. However, health consumers could be easily overwhelmed by the overloaded information. Healthcare information searching can be very difficult for consumers, not to mention most of them are not skilled information searcher. In this work, we investigate the approaches for measuring user similarity in online health social websites. By recommending similar users to consumers, we can help them to seek informational and emotional support in a more efficient way. Methods: We propose to represent the healthcare social media data as a heterogeneous healthcare information network and introduce the local and global structural approaches for measuring user similarity in a heterogeneous network. We compare the proposed structural approaches with the content-based approach. Results: Experiments were conducted on a data set collected from a popular online health social website,and the results showed that content-based approach performed better for inactive users, while structural approaches performed better for active users. Moreover, global structural approach outperformed local structural approach for all user groups. In addition, we conducted experiments on local and global structural approaches using different weight schemas for the edges in the network. Leverage performed the best for both local and global approaches. Finally, we integrated different approaches and demonstrated that hybrid method yielded better performance than the individual approach. Conclusion: The results indicate that content-based methods can effectively capture the similarity of inactive users who usually have focused interests, while structural methods can achieve better performance when rich structural information is available. Local structural approach only considers direct connections between nodes in the network, while global structural approach takes the indirect connections into account. Therefore, the global similarity approach can deal with sparse networks and capture the implicit similarity between two users. Different approaches may capture different aspects of the similarity relationship between two users. When we combine different methods together, we could achieve a better performance than using each individual method.