scholarly journals Model-based testing of global properties on large-scale distributed systems

2014 ◽  
Vol 56 (7) ◽  
pp. 749-762 ◽  
Author(s):  
Gerson Sunyé ◽  
Eduardo Cunha de Almeida ◽  
Yves Le Traon ◽  
Benoit Baudry ◽  
Jean-Marc Jézéquel
Keyword(s):  
Distributed Systems ◽  
Large Scale ◽  
Model Based ◽  
Global Properties ◽  
Model Based Testing

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

2020 ◽  
Author(s):  
Wilfried Yves Hamilton Adoni ◽  
Moez Krichen ◽  
Tarik Nahhal ◽  
Abdeltif Elbyed
Keyword(s):  
Time Complexity ◽  
Large Scale ◽  
Computation Time ◽  
Final States ◽  
Memory Design ◽  
Model Based ◽  
Distributed Approach ◽  
Distributed Framework ◽  
Model Based Testing ◽  
Testing Theory

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.

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

2020 ◽  
Author(s):  
Wilfried Yves Hamilton Adoni ◽  
Moez Krichen ◽  
Tarik Nahhal ◽  
Abdeltif Elbyed
Keyword(s):  
Time Complexity ◽  
Large Scale ◽  
Computation Time ◽  
Final States ◽  
Memory Design ◽  
Model Based ◽  
Distributed Approach ◽  
Distributed Framework ◽  
Model Based Testing ◽  
Testing Theory

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.

Model-Based Testing of Real-Time Distributed Systems

2016 ◽  
pp. 272-286 ◽  
Author(s):  
Jüri Vain ◽  
Evelin Halling ◽  
Gert Kanter ◽  
Aivo Anier ◽  
Deepak Pal
Keyword(s):  
Distributed Systems ◽  
Real Time ◽  
Model Based ◽  
Model Based Testing

Model-based Control Techniques for Large-Scale High-Precision Stage

2020 ◽  
Vol 140 (4) ◽  
pp. 272-280
Author(s):  
Wataru Ohnishi ◽  
Hiroshi Fujimoto ◽  
Koichi Sakata
Keyword(s):  
High Precision ◽  
Large Scale ◽  
Model Based Control ◽  
Precision Stage ◽  
Control Techniques ◽  
Model Based
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