  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 single-hunk 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.
