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Traditional coverage grey-box fuzzers perform a breadth-first search of thestate space of Program Under Test (PUT). This aimlessness wastes a lot ofcomputing resources. Directed grey-box fuzzing focuses on the target of PUT andbecomes one of the most popular topics of software testing. The earlytermination of unreachable test cases is a method to improve directed grey-boxfuzzing. However, existing solutions have two problems: firstly, reachabilityanalysis needs to introduce extra technologies (e.g., static analysis);secondly, the performance of reachability analysis and auxiliary technologieslack versatility.  We propose FGo, a probabilistic exponential cut-the-loss directed grey-boxfuzzer. FGo terminates unreachable test cases early with exponentiallyincreasing probability. Compared to other technologies, FGo makes full use ofthe unreachable information contained in iCFG and doesn't generate anyadditional overhead caused by reachability analysis. Moreover, it is easy togeneralize to all PUT. This strategy based on probability is perfectly adaptedto the randomness of fuzzing.  The experiment results show that FGo is 106% faster than AFLGo in reproducingcrashes. We compare multiple parameters of probabilistic exponentialcut-the-loss algorithm and analyze them in detail. In addition, for enhancingthe inerpretability of FGo, this paper discusses the difference between thetheoretical performance and the practical performance of probabilisticexponential cut-the-loss algorithm.

FGo: A Directed Grey-box Fuzzer with Probabilistic Exponential cut-the-loss Strategies