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Fuzz testing (fuzzing) is a well-known method for exposingbugs/vulnerabilities in software systems. Popular fuzzers, such as AFL, use abiased random search over the domain of program inputs, where 100s or 1000s ofinputs (test cases) are executed per second in order to expose bugs. If a bugis discovered, it can either be fixed manually by the developer or fixedautomatically using an Automated Program Repair (APR) tool. Like fuzzing, manyexisting APR tools are search-based, but over the domain of patches rather thaninputs.  In this paper, we propose search-based program repair as patch-level fuzzing.The basic idea is to adapt a fuzzer (AFL) to fuzz over the patch space ratherthan the input space. Thus we use a patch-space fuzzer to explore a patchspace, while using a traditional input level fuzzer to rule out patchcandidates and help in patch selection. To improve the throughput, we propose acompilation-free patch validation methodology, where we execute the original(unpatched) program natively, then selectively interpret only the specificpatched statements and expressions. Since this avoids (re)compilation, we showthat compilation-free patch validation can achieve a similar throughput asinput-level fuzzing (100s or 1000s of execs/sec). We show that patch-levelfuzzing and input-level fuzzing can be combined, for a co-exploration of bothspaces in order to find better quality patches. Such a collaboration betweeninput-level fuzzing and patch-level fuzzing is then employed to search overcandidate fix locations, as well as patch candidates in each fix location.

Program Repair by Fuzzing over Patch and Input Space