  Being able to automatically repair programs is an extremely challenging task.
In this paper, we present MintHint, a novel technique for program repair that
is a departure from most of today's approaches. Instead of trying to fully
automate program repair, which is often an unachievable goal, MintHint performs
statistical correlation analysis to identify expressions that are likely to
occur in the repaired code and generates, using pattern-matching based
synthesis, repair hints from these expressions. Intuitively, these hints
suggest how to rectify a faulty statement and help developers find a complete,
actual repair. MintHint can address a variety of common faults, including
incorrect, spurious, and missing expressions.
  We present a user study that shows that developers' productivity can improve
manyfold with the use of repair hints generated by MintHint -- compared to
having only traditional fault localization information. We also apply MintHint
to several faults of a widely used Unix utility program to further assess the
effectiveness of the approach. Our results show that MintHint performs well
even in situations where (1) the repair space searched does not contain the
exact repair, and (2) the operational specification obtained from the test
cases for repair is incomplete or even imprecise.
