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This leads to a potential high-number of scenarios to be tested. For the previous example, and considering a model where parameters have just a limited number of possible values, that would still lead to 3*3*3*2*3=162 scenarios.
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System produce produces different results not only due to changes on the input parameters but also due to using the system on different contexts (e.g., configurations, operating system, timezones, cloud provider). |
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- takes considerable time
- is costly from interms of human resources or infrastructure perspective
- may be innefficient (more info ahead)
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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.
Pairwise (2-way) and
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way Testing
Given the empyrical data mentioned earlier, adopting pairwise testing to test all the ombinations of pairs of parameters (sometimes also called as "all pairs testing") is a technique that is not only feasible but also provides great results in terms of fault-detection.
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Imagining the previous example, instead of having XXX 162 test scenarios to perform, we would need just XX13 using pairwise .
Sometimes, we may need to test more thoroughly some parameters, and for those we may choose to 3-way testing, for example, to ensure that we cover all the combinations of values of 3 relevant parameters. In this case we may have mixed-strength scenarios where combinations of certain parameters may be tested more thoroughly than others.
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