Multi-path Coverage of all Final States for Model-Based Testing Theory
using Spark In-memory Design
This paper deals with an efficient and robust distributed framework for
finite state machine coverage in the field model based testing theory.
All final states coverage in large-scale automaton is inherently
computing-intensive and memory exhausting with impractical time
complexity because of an explosion of the number of states. Thus, it is
important to propose a faster solution that reduces the time complexity
by exploiting big data concept based on Spark RDD computation. To cope
with this situation, we propose a parallel and distributed approach
based on Spark in-memory design which exploits A* algorithm for optimal
coverage. The experiments performed on multi-node cluster prove that the
proposed framework achieves significant gain of the computation time.