We present JOG, a framework that facilitates developing Java JIT
peephole optimizations alongside JIT tests. JOG enables developers
to write a pattern, in Java itself, that specifies desired code transformations by writing code before and after the optimization, as
well as any necessary preconditions. Such patterns can be written
in the same way that tests of the optimization are already written
in OpenJDK. JOG translates each pattern into C/C++ code that
can be integrated as a JIT optimization pass. JOG also generates
Java tests for optimizations from patterns. Furthermore, JOG can
automatically detect possible shadow relation between a pair of
optimizations where the effect of the shadowed optimization is
overridden by another. Our evaluation shows that JOG makes it
easier to write readable JIT optimizations alongside tests without
decreasing the effectiveness of JIT optimizations. We wrote 162
patterns, including 68 existing optimizations in OpenJDK, 92 new
optimizations adapted from LLVM, and two new optimizations that
we proposed. We opened eight pull requests (PRs) for OpenJDK,
including six for new optimizations, one on removing shadowed
optimizations, and one for newly generated JIT tests; seven PRs
have already been integrated into the master branch of OpenJDK.