Multi-path Coverage of all Final States for Model-Based Testing Theory
using Spark In-memory Design
Abstract
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.