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sequence_mode [2020/05/10 02:50]
admin [Sequencers] 		sequence_mode [2021/01/05 03:00] (current)
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 //Sequence Mode// is the test case generation algorithm used to generate test cases and test sequences from the model. //TestOptimal// supports several algorithms ranging from random walk to optimal sequencer and custom test case generation.  //Sequence Mode// is the test case generation algorithm used to generate test cases and test sequences from the model. //TestOptimal// supports several algorithms ranging from random walk to optimal sequencer and custom test case generation. 
  
----- +//TestOptimal// supports 4 sequencers to generate test sequences or test cases from the model. All of them can be used for both //Online MBT Testing// and //Offline MBT Testing//.  
-==== Sequencers ==== +
- +
-//TestOptimal// supports 7 sequencers to generate test sequences or test cases from the model. All of them can be used for both //Online MBT Testing// and //Offline MBT Testing//.  +
  
 Depending on your testing objective, some sequencers may produce better or more desirable test cases from your model than others. You should experiment with these sequencers for each model to determine which sequencer meets your needs better. Depending on your testing objective, some sequencers may produce better or more desirable test cases from your model than others. You should experiment with these sequencers for each model to determine which sequencer meets your needs better.
  
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-=== Optimal Sequencer ===+==== Optimal Sequencer ====
  
 //Optimal Sequencer// generates the test sequence with minimal number of steps that covers all of the transitions in the model by applying //Chinese Postman Problem//. //Optimal Sequencer// generates the test sequence with minimal number of steps that covers all of the transitions in the model by applying //Chinese Postman Problem//.
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-=== Random Sequencer ===+==== Random Sequencer ====
  
 //Random Sequencer// generates test path by walking the model randomly.  The transitions are selected for traversal based on the assigned weight.  The higher the weight, the more often will it be traversed. //Random Sequencer// generates test path by walking the model randomly.  The transitions are selected for traversal based on the assigned weight.  The higher the weight, the more often will it be traversed.
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-=== MCase Sequencer ===+==== MCase Sequencer ====
  
 //MCase Sequencer// generates the shortest test sequence to complete the navigation path described for each //MCase//. The navigation steps defined in each MCase need not be consecutive, the system will find the shortest path to fill the gap with transitions from the model. //MCase Sequencer// generates the shortest test sequence to complete the navigation path described for each //MCase//. The navigation steps defined in each MCase need not be consecutive, the system will find the shortest path to fill the gap with transitions from the model.
  
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-=== Choosing Right Sequencer ===+==== Choosing Right Sequencer ====
  
 Different sequencer produces test sequence or test cases resulting in different effects on test coverage.  You should evaluate them and determine which sequencer is a better choice for each model and test objective.  Keep in mind that you may change the sequencer anytime. Different sequencer produces test sequence or test cases resulting in different effects on test coverage.  You should evaluate them and determine which sequencer is a better choice for each model and test objective.  Keep in mind that you may change the sequencer anytime.