  We propose TRANSMUT-Spark, a tool that automates the mutation testing process
of Big Data processing code within Spark programs. Apache Spark is an engine
for Big Data Processing. It hides the complexity inherent to Big Data parallel
and distributed programming and processing through built-in functions,
underlying parallel processes, and data management strategies. Nonetheless,
programmers must cleverly combine these functions within programs and guide the
engine to use the right data management strategies to exploit the large number
of computational resources required by Big Data processing and avoid
substantial production losses. Many programming details in data processing code
within Spark programs are prone to false statements that need to be correctly
and automatically tested. This paper explores the application of mutation
testing in Spark programs, a fault-based testing technique that relies on fault
simulation to evaluate and design test sets. The paper introduces the
TRANSMUT-Spark solution for testing Spark programs. TRANSMUT-Spark automates
the most laborious steps of the process and fully executes the mutation testing
process. The paper describes how the tool automates the mutants generation,
test execution, and adequacy analysis phases of mutation testing with
TRANSMUT-Spark. It also discusses the results of experiments that were carried
out to validate the tool to argue its scope and limitations.
