Traditional automatic program repair (APR) tools rely on a test-suite as a repair specification. But test suites even when available are not of specification quality, limiting the performance and hence viability of test-suite based repair. On the other hand, static analysis-based bug finding tools are seeing increasing adoption in industry but still face challenges since the reported violations are viewed as not easily actionable. We propose a novel solution that solves both these challenges through a technique for automatically generating high-quality patches for static analysis violations by learning from examples. Our approach uses the static analyzer as an oracle and does not require a test suite. We realize our solution in a system, Phoenix, that implements a fully-automated pipeline that mines and cleans patches for static analysis violations from the wild, learns generalized executable repair strategies as programs in a novel Domain Specific Language (DSL), and then instantiates concrete repairs from them on new unseen violations. Using Phoenix we mine a corpus of 5,389 unique violations and patches from 517 Github projects. In a cross-validation study on this corpus Phoenix successfully produced 4,596 bug-fixes, with a recall of 85% and a precision of 54%. When applied to the latest revisions of a further5 Github projects, Phoenix produced 94 correct patches to previously unknown bugs, 19 of which have already been accepted and merged by the development teams. To the best of our knowledge this constitutes, by far the largest application of any automatic patch generation technology to large-scale real-world systems

@inproceedings{paperbot5165,
  title = {Phoenix: automated data-driven synthesis of repairs for static analysis violations},
  author = {Rohan Bavishi and Hiroaki Yoshida and Mukul R. Prasad},
  booktitle = {FSE},
  year = {2019},
  doi = {10.1145/3338906.3338952}
}