  Datalog is a popular and widely-used declarative logic programming language.
Datalog engines apply many cross-rule optimizations; bugs in them can cause
incorrect results. To detect such optimization bugs, we propose an automated
testing approach called Incremental Rule Evaluation (IRE), which
synergistically tackles the test oracle and test case generation problem. The
core idea behind the test oracle is to compare the results of an optimized
program and a program without cross-rule optimization; any difference indicates
a bug in the Datalog engine. Our core insight is that, for an optimized,
incrementally-generated Datalog program, we can evaluate all rules individually
by constructing a reference program to disable the optimizations that are
performed among multiple rules. Incrementally generating test cases not only
allows us to apply the test oracle for every new rule generated-we also can
ensure that every newly added rule generates a non-empty result with a given
probability and eschew recomputing already-known facts. We implemented IRE as a
tool named Deopt, and evaluated Deopt on four mature Datalog engines, namely
Souffl\'e, CozoDB, $\mu$Z, and DDlog, and discovered a total of 30 bugs. Of
these, 13 were logic bugs, while the remaining were crash and error bugs. Deopt
can detect all bugs found by queryFuzz, a state-of-the-art approach. Out of the
bugs identified by Deopt, queryFuzz might be unable to detect 5. Our
incremental test case generation approach is efficient; for example, for test
cases containing 60 rules, our incremental approach can produce 1.17$\times$
(for DDlog) to 31.02$\times$ (for Souffl\'e) as many valid test cases with
non-empty results as the naive random method. We believe that the simplicity
and the generality of the approach will lead to its wide adoption in practice.
