Some things remain useful regardless of their age. For QA testers, cause-effect graphs are flexible, fast and effective,...
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which makes them especially useful for Agile testing.
Although the essence of Agile is generally speed, accuracy and validity are also important. In other words, you can test as fast you'd like, but if the testing is not documented (re-usable) or valid, it's next to useless.
Cause-effect graphing is an old school, black box QA testing technique that fits well in Agile testing environments. Once you understand the graphing concept, creating re-usable diagrams as test case documentation is fast and convenient. A QA tester can pack a whole battery of tests into a single, readable, understandable diagram. Cause-effect graphs are not flow charts, where one can get lost in the directional arrows; these graphs are simple and straightforward.
Also known as an Ishikawa or fishbone diagram, a cause-effect graph displays an outcome and all of the factors that influence that outcome. In other words, for QA, it displays all of the expected scenarios for testing. See the basic example below.
Cause-effect graphing can also leverage team knowledge. Because these graphs are generally straightforward, it is easier for developers and QA to ensure their team understands the process and, thus, that the testing is both valid and complete.
These diagrams are also handy for displaying unknown scenarios that may require developer investigation, such as when the effect or outcome is not defined. In this way, defects are found and fixed before code is complete.
Additionally, cause-effect diagrams provide effective design documentation. Agile QA teams can create not only a full suite of test cases with a single diagram, they can also create the feature documentation. Because the diagrams are generally easy to follow and understand, they are especially useful for sharing with extended team members in support and product management.
Try out this old school method and see how well it fits in your Agile QA testing. It may save time, effort and generate more valuable test cases, as well as re-usable feature documentation. QAs can spend more time testing and less time writing or identifying complex testing scenarios when using cause-effect graphing.
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