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Systems and features with some sort of variation
Systems in general, including software and hardware, usually have some input parameters that will affect the output(s) produced by the system. The number of parameters and the possible values, their ranges, vary and can be limited, huge but finite, or even infinite.
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In this tutorial we'll learn about the testing challenges of these systems and how to overcome them efficiently.
Initial testing options
Test using some examples for the parameters
The first strategy that we may come with would be adopting data-driven testing.
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Xray has built-in support for datasets where testers can explicitly enumerate parameters and the combination of values to be tested.
Please see Parameterized Tests for more info. |
Test every parameter/value combination
Testing every possible combination of parameters is only viable if we have very few parameters with very few possible values for each one of them.
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Xray also supports combinatorial parameters, where the user defines the values for each parameter and Xray calculates all the possible combinations, turning that into the dataset to be used.
It's possible to remove some values of the combinations to be generated. For example, we can exclude the "First" Class. That would lead to less scenarios to test (e.g., 162 => 108) but could still not be enough if we aim to have a limited set of tests.
Please see Parameterized Tests for more info. |
Test using random combination of parameter values
Random testing is always an option that comes but it doesn't ensure we test combinations that matter unless we perform a very high number of tests, which would probabilisticly include a certain % of combinations or even all of them if we spend an infinite time randomly testing.
Nobody wants to perform testing endlessly, without any sort of criteria. Random testing doesn't ensure we cover combinations that matter with a very limited set of tests.
Empyrical data about fault detection
Studies, such as presented by NIST, PRACTICAL COMBINATORIAL TESTING, 2010, indicate that the vast majority of defects (67%-93%) related to input values are due to either to a problem in a parameter value (single-value fault) or in a combination of two parameter values (2-way interaction fault).
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Bugs related to the interaction of more parameters decrease with the number of parameters; in other words, finding these rare bugs will require much more tests to be performed, leading to more time/costs. However, those rare t-way interaction faults can also be critical.
Combinatorial Testing considering 2-way (pairwise) and t-way interaction of parameters
Combinatorial testing is at first sight a technique that seems adequate precisely for this purpose, as the name shows.
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There are different algorithms for generating t-way interaction of parameters (e.g., pairs, triplets). Different tools/algorithms may generate more scenarios than others to achieve the same coverage (in terms of interaction of parameters), and take more or less time. |
Reducing the number of test scenarios
Imagining the previous example, instead of having 162 test scenarios to perform, we could have just 13 using pairwise to generate them.
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In sum, there is a balance between the number of tests we execute and the coverage of interactions between variables (i.e., "t-way coverage") we validate.
Optimizing further the test scenarios to be performed
The first level of optimization is further reducing the number of generated test scenario.
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In Test Case Designer we can . In the following example, we considered an interaction that we need to test due to an hypothetic legislation where some warning must be shown to users who are departing from USA, using the First class, and have more than 1 children. That scenario will be added on the generated ones.
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Using pairwise and t-way for scripted testing and exploratory testing
Whenever generating an optimized dataset (i.e., multiple "rows" of values for the parameters) this will be typically used to data-drive a scripted test case (e.g., a "manual" test composed of steps, or an automated test script).
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Pairwise and t-way testing don't tell us how to actually perform testing; it just generates the combination of parameters. Therefore, we can use this technique also if choose to adopt a more exploratory testing approach, for example for certain configurations of hardware/software.
Challenges
Pairwise, or t-way testing in general, even though useful, it's not is not a silver bullet.
Some challenges or limitations to be aware of, include:
- test oracle: this technique doesn't address finding the proper test oracle for the generated scenarios. How do we know the scenario is behaving as expected? How do we know that a given scenario has issues or not?
- modeling: depicting a "good" model requires the intervention of testers. Testers with the help of other team members are the ones able to figure out representative and important scenarios to model , their parameters, the values for those parameters, constraints, etc.
Xray datasets and Xray Test Case Designer
Xray has built-in support for parameterized tests and datasets, supporting user-defined datasets and automatic generation of combinations for the identified parameters.
Test Case Designer (TCD) provides a more comprehensive modeling tool, where it's possible to define parameters, apply constraints, enforce specific scenarios, generate optimized datasets/scenarios using 2-way or nt-way to ensure that certain parameters are tested together, up to a certain level. With Test Case Designer it's possible to have a limited and manageabled set of test scenarios to perform and make sure that most combination of parameters are met with the initial scenarios, so that most risk is addressed upfront.
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