DeepFuzz: Automatic Generation of Syntax Valid C Programs for Fuzz Testing
Xiao Liu, Xiaoting Li, Rupesh Prajapati, Dinghao Wu. AAAI 2019
Compilers are among the most fundamental programming tools for building software. However, production compilers remain buggy. Fuzz testing is often leveraged with newly-generated, or mutated inputs in order to find new bugs or security vulnerabilities. In this paper, we propose a grammar-based fuzzing tool called DeepFuzz. Based on a generative Sequence-to-Sequence model, DeepFuzz automatically and continuously generates well-formed C programs. We use this set of new C programs to fuzz off-the-shelf C compilers, e.g. GCC and Clang/LLVM. We present a detailed case study to analyze the success rate and coverage improvement of the generated C programs for fuzz testing. We analyze the performance of DeepFuzz with three types of sampling methods as well as three types of generation strategies. Consequently, DeepFuzz improved the testing efficacy in regards to the line, function, and branch coverage. In our preliminary study, we found and reported 8 bugs of GCC, all of which are actively being addressed by developers.
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