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Automated Program Repair (APR) techniques typically rely on a giventest-suite to guide the repair process. Apart from the need to provide testoracles, this makes the produced patches prone to test data over-fitting. Inthis work, instead of relying on test cases, we show how to automaticallyrepair memory safety issues, by leveraging static analysis (specificallyIncorrectness Separation Logic) to guide repair. Our proposed approach learnswhat a desirable patch is by inspecting how close a patch is to fixing the bugbased on the feedback from incorrectness separation logic based static analysis(specifically the Pulse analyser), and turning this information into adistribution of probabilities over context free grammars. Furthermore, insteadof focusing on heuristics for reducing the search space of patches, we makerepair scalable by creating classes of equivalent patches according to theeffect they have on the symbolic heap, and then invoking the validation oracleonly once per class of patch equivalence. This allows us to efficientlydiscover repairs even in the presence of a large pool of patch candidatesoffered by our generic patch synthesis mechanism. Experimental evaluation ofour approach was conducted by repairing real world memory errors in OpenSSL,swoole and other subjects. The evaluation results show the scalability andefficacy of our approach in automatically producing high quality patches.

Patch Space Exploration using Static Analysis Feedback