Automated program repair relying on static analysis complements test-driven repair, since it does not require failing tests to repair a bug, and it avoids test-overfitting by considering program properties. Due to the rich variety and complexity of program analyses, existing static program repair techniques are tied to specific analysers, and thus repair only narrow classes of defects. To develop a general-purpose static program repair framework that targets a wide range of properties and programming languages, we propose to integrate program repair with Datalog-based analysis. Datalog solvers are programmable fixed point engines which can be used to encode many program analysis problems in a modular fashion. The program under analysis is encoded as Datalog facts, while the fixed point equations of the program analysis are expressed as recursive Datalog rules. In this context, we view repairing the program as modifying the corresponding Datalog facts. This is accomplished by a novel technique, symbolic execution of Datalog, that evaluates Datalog queries over a symbolic database of facts, instead of a concrete set of facts. The result of symbolic query evaluation allows us to infer what changes to a given set of Datalog facts repair the program so that it meets the desired analysis goals. We developed a symbolic executor for Datalog called Symlog, on top of which we built a repair tool SymlogRepair. We show the versatility of our approach on several analysis problems --- repairing null pointer exceptions in Java programs, repairing data leaks in Python notebooks, and repairing four types of security vulnerabilities in Solidity smart contracts.

@inproceedings{LiuMSR23,
  title = {Program Repair Guided by Datalog-Defined Static Analysis},
  author = {Yu Liu and Sergey Mechtaev and Pavle Subotić and Abhik Roychoudhury},
  booktitle = {FSE},
  year = {2023},
  doi = {10.1145/3611643.3616363}
}