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- | ====== Tutorial: Model Sequencers - pros and cons ====== | ||
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- | Learning Objectives: | ||
- | * [[# | ||
- | * [[#Types of Sequencers]] | ||
- | * [[# | ||
- | * [[# | ||
- | * [[# | ||
- | * [[# | ||
- | * [[# | ||
- | * [[# | ||
- | * [[# | ||
- | * [[# | ||
- | * [[# | ||
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- | ===== What's Model Sequencer? ===== | ||
- | //Model Sequencer// is algorithm-based the test sequence (test cases) generation from the model. | ||
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- | The test sequence (test case) generation is referred to as // | ||
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- | The generated test cases can be automated to drive Application Under Test (AUT) through // | ||
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- | =====Types of Sequencers===== | ||
- | The algorithms used by the sequencers differ depend on the type of models: // | ||
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- | For // | ||
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- | Different algorithms achieve different coverage on the model. In combination with //stop conditions//, | ||
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- | For // | ||
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- | =====Sequencer - Random===== | ||
- | //Random// sequencer traverses // | ||
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- | The weight of the outgoing transitions determines the probability a transition will be traversed from its source state. | ||
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- | //Random// sequencer is useful for exploratory type of testing by allowing the model to run for an extended period of time to let the algorithm traverse "all possible" | ||
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- | //Random// sequencer is also often used for load and stress testing by running model with may //virtual users (VU)// to simulate realistic load of a production systems. | ||
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- | Another good use of //Random// sequencer is to find memory leak, locking contention, etc. that are caused by running //AUT// over a long period of time. You can just leave the model running with the elapse time stop condition set to desired duration of time. | ||
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- | The disadvantage of //Random// sequencer is that the model coverage is typically time-dependent - it may take longer time for it to achieve the desired model coverage. | ||
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- | =====Sequencer - Optimal===== | ||
- | //Optimal// sequencer uses uses algorithms for [[https:// | ||
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- | The obvious advantage of //Optimal// sequencer is that it generates the least number of steps to cover every transitions in the model. | ||
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- | The side effect is that the test cases generated by //Optimal// sequencer tends to be longer and fewer test cases with the minimal total number of test steps. | ||
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- | =====Sequencer - Priority Path===== | ||
- | // | ||
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- | The test cases generated by // | ||
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- | // | ||
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- | =====Sequencer - Pairwise===== | ||
- | // | ||
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- | // | ||
- | =====Sequencer - Mark Optimal===== | ||
- | Often times you may want to only test certain part of the model. | ||
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- | //Mark Optimal// sequencer generates the test cases using the same [https:// | ||
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- | //Mark Optimal// sequencer (and [[# | ||
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- | //Mark Optimal// sequencer is only available when running model in IDE. | ||
- | =====Sequencer - Mark Serial===== | ||
- | //Mark Serial// sequencer is similar to [[# | ||
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- | What distinguishes //Mark Serial// sequencer from //Mark Optimal// sequencer is that the test cases generates follows the order the transitions are marked. | ||
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- | //Mark Serlia// sequencer (and [[# | ||
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- | //Mark Serial// sequencer allows you to test scenarios that require traversing transitions in a specific order. | ||
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- | //Mark Optimal// sequencer is only available when running model in IDE. | ||
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- | =====Sequencer - MCase===== | ||
- | //MCase// is a collection of transitions that represents a custom test case. //MCase// sequencer generates the actual test sequence (test steps) to navigate through the model to cover the transitions included in the //MCase//. | ||
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- | The main use of //MCase// sequencer is to generate custom test cases, which can be used to test a bug fix, regression testing of important workflows and smoke testing. | ||
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- | Since //MCase// is built with scripting, you can dynamically build any number of //MCases// through //REST// api. This provides an option to allow ALM process to test bug fixes remotely. | ||
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- | =====Sequencer - Concurrent===== | ||
- | // | ||
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