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sequence_mode [2020/05/10 02:51] admin |
sequence_mode [2021/01/05 03:00] |
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- | ===== Sequence Modes (Sequencers) ===== | ||
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- | //Sequence Mode// is the test case generation algorithm used to generate test cases and test sequences from the model. // | ||
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- | // | ||
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- | 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 ==== | ||
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- | //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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- | If used with //Mark// mode, it will generate test sequence to cover currently //marked// states and transitions. | ||
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- | ==== Random Sequencer ==== | ||
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- | //Random Sequencer// generates test path by walking the model randomly. | ||
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- | This sequencer is very effective for online testing (test automation). It continuously generates test sequence until a stop condition is met. This is typically used for load and stress testing to find defects caused by long running system like memory leak or deadlock situation or simulate production load. | ||
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- | ==== PriorityPath Sequencer ==== | ||
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- | // | ||
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- | The default transition weight is 5. It is recommended that high priority transitions be assigned a value at least 10 for small models and much larger value for larger models. | ||
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- | You may increase the sensitivity of the sequencer (how aggressive high priority transitions are chosen first) by increasing the gap of the weight values between high priority transition and the rest of the transitions. | ||
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- | ==== MCase Sequencer ==== | ||
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- | //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, | ||
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- | ==== Choosing Right Sequencer ==== | ||
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- | Different sequencer produces test sequence or test cases resulting in different effects on test coverage. | ||
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- | For example if your goal is to ensure all transitions are tested, [[#Optimal Sequencer]] or [[# | ||
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- | If you are looking for testing different variations and paths of the model and expose AUT to long running scenarios, then [[#Random Seqencer]] may suit your needs better. | ||
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