  The emerging data-intensive applications are increasingly dependent on
data-intensive scalable computing (DISC) systems, such as Apache Spark, to
process large data. Despite their popularity, DISC applications are hard to
test. In recent years, fuzz testing has been remarkably successful; however, it
is nontrivial to apply such traditional fuzzing to big data analytics directly
because: (1) the long latency of DISC systems prohibits the applicability of
fuzzing, and (2) conventional branch coverage is unlikely to identify
application logic from the DISC framework implementation. We devise a novel
fuzz testing tool called BigFuzz that automatically generates concrete data for
an input Apache Spark program. The key essence of our approach is that we
abstract the dataflow behavior of the DISC framework with executable
specifications and we design schema-aware mutations based on common error types
in DISC applications. Our experiments show that compared to random fuzzing,
BigFuzz is able to speed up the fuzzing time by 1477X, improves application
code coverage by 271%, and achieves 157% improvement in detecting application
errors. The demonstration video of BigFuzz is available at
https://www.youtube.com/watch?v=YvYQISILQHs&feature=youtu.be.
