JITfuzz: Coverage-guided Fuzzing for JVM Just-in-Time Compilers

Abstract

As a widely-used platform to support various Java-bytecode-based applications, Java Virtual Machine (JVM) incurs severe performance loss caused by its real-time program interpretation mechanism. To tackle this issue, the Just-in- Time compiler (JIT) has been widely adopted to strengthen the efficacy of JVM. Therefore, how to effectively and efficiently detect JIT bugs becomes critical to ensure the correctness of JVM. In this paper, we propose a coverage-guided fuzzing framework, namely JITfuzz, to automatically detect JIT bugs. In particular, JITfuzz adopts a set of optimization-activating mutators to trigger the usage of typical JIT optimizations, e.g., function inlining and simplification. Meanwhile, given JIT optimizations are closely coupled with program control flows, JITfuzz also adopts mutators to enrich the control flows of target programs. Moreover, JITfuzz also proposes a mutator scheduler which iteratively schedules mutators according to the coverage updates to maximize the code coverage of JIT. To evaluate the effectiveness of JITfuzz, we conduct a set of experiments based on a benchmark suite with 16 popular JVM-based projects from GitHub. The experimental results suggest that JITfuzz outperforms the state-of-the-art mutation-based and generation-based JVM fuzzers by 27.9 % and 18.6 % respectively in terms of edge coverage on average. Furthermore, JITfuzz also successfully detects 36 previously unknown bugs (including 23 JIT bugs) and 27 bugs (including 18 JIT bugs) have been confirmed by the developers.