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.