Multi-path Coverage of All Final States for Model-Based Testing Theory Using Spark In-memory Design

2020 ◽  
pp. 195-204
Author(s):  
Wilfried Yves Hamilton Adoni ◽  
Moez Krichen ◽  
Tarik Nahhal ◽  
Abdeltif Elbyed
Keyword(s):  
Final States ◽  
Memory Design ◽  
Model Based ◽  
Model Based Testing ◽  
Testing Theory ◽  
Path Coverage

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 for Web Applications

2011 ◽  
Vol 34 (6) ◽  
pp. 1012-1028 ◽  
Author(s):  
Huai-Kou MIAO ◽  
Sheng-Bo CHEN ◽  
Hong-Wei ZENG
Keyword(s):  
Web Applications ◽  
Model Based ◽  
Model Based Testing

Applying model-based testing to healthcare products

2008 ◽  
Author(s):  
Marlon Vieira ◽  
Xiping Song ◽  
Gilberto Matos ◽  
Stephan Storck ◽  
Rajanikanth Tanikella ◽  
...  
Keyword(s):  
Model Based ◽  
Model Based Testing
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