  Fuzzing is becoming more and more popular in the field of vulnerability
detection. In the process of fuzzing, seed selection strategy plays an
important role in guiding the evolution direction of fuzzing. However, the SOTA
fuzzers only focus on individual uncertainty, neglecting the multi-factor
uncertainty caused by both randomization and evolution. In this paper, we
consider seed selection in fuzzing as a large-scale online planning problem
under uncertainty. We propose \mytool which is a new intelligent seed selection
strategy. In Alpha-Fuzz, we leverage the MCTS algorithm to deal with the
effects of the uncertainty of randomization and evolution of fuzzing.
Especially, we analyze the role of the evolutionary relationship between seeds
in the process of fuzzing, and propose a new tree policy and a new default
policy to make the MCTS algorithm better adapt to the fuzzing. We compared
\mytool with four state-of-the-art fuzzers in 12 real-world applications and
LAVA-M data set. The experimental results show that \mytool could find more
bugs on lava-M and outperforms other tools in terms of code coverage and number
of bugs discovered in the real-world applications. In addition, we tested the
compatibility of \mytool, and the results showed that \mytool could improve the
performance of existing tools such as MOPT and QSYM.
