Despite significant advances in automatic program repair (APR) techniques over the past decade, practical deployment remains an elusive goal. One of the important challenges in this regard is the general inability of current APR techniques to produce patches that require edits in multiple locations, i.e., multi-hunk patches. In this work, we present a novel APR technique that generalizes singlehunk repair techniques to include an important class of multi-hunk bugs, namely bugs that may require applying a substantially similar patch at a number of locations. We term such sets of repair locations as evolutionary siblings -similar looking code, instantiated in similar contexts, that are expected to undergo similar changes. At the heart of our proposed method is an analysis to accurately identify a set of evolutionary siblings, for a given bug. This analysis leverages three distinct sources of information, namely the test-suite spectrum, a novel code similarity analysis, and the revision history of the project. The discovered siblings are then simultaneously repaired in a similar fashion. We instantiate this technique in a tool called Hercules and demonstrate that it is able to correctly fix 49 bugs in the Defects4J dataset, the highest of any individual APR technique to date. This includes 15 multi-hunk bugs and overall 13 bugs which have not been fixed by any other technique so far. CCS CONCEPTS• Software and its engineering → Software testing and debugging; KEYWORDS Automatic Program Repair, Machine Learning, OOP, Multi-hunk patches, Code Similarity † This work was done when the author was an intern at Fujitsu Laboratories of America. 1 We use evolution as a metaphor for the environment, i.e., context, of a piece of code, in addition to the changes it undergoes over its lifetime.

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performance against state-of-the-art tools as well as its effectiveness and scalability on a set of sizable, realistic Java server-client and peer-to-peer applications.

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