The previous two questions described issues surrounding languages that support test case development and the ways that results are interpreted. However, these only briefly touch on issues of automation itself. While all test tools are expected to automate the execution itself, these activities only cover one subset of a test workflow. In this section, we&#;ll cover three other aspects of testing that can and should be automated:
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As automation scales up and integrates additional functionality, this leads to greater productivity and output, while reducing resource costs. For example, automated scheduling can reduce the number of test automation licenses needed by assigning certain tests to Agents or Bots under one license, which can execute the same amount of test cases as multiple test engineers. Likewise, automating test case creation helps teams start testing sooner and reduce the amount of time spent implementing changes or fixes across entire projects.
A commonality of these three features or tools is that they free up time used on manual tasks, enabling teams to spend more time improving their workflows and solving defects. Each feature helps improve reliability and validity of tests and their results by preventing human error as well as generating additional data that can be helpful for identifying what works as expected and what doesn&#;t.
Creating a suite of test cases for a project is a time-consuming activity, but absolutely necessary for ensuring proper test coverage. For example, test case development may include manually transforming requirements into steps or models, which makes test cases prone to human error such as typos and incorrect interpretations of requirements. One solution to this problem is using model-based test tools that can also help with scaling up test coverage.
Model-based testing (MBT) derives suites of test cases from models and test requirements. These models often look like flow charts or decision tables, and include components such as:
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Once defined, the model&#;s output consists of `X` amount of test cases. These can be imported into a test automation application and executed, or may be automatically deployed to an intelligent scheduler.
A huge benefit of MBT is that models can be tweaked or updated and the changes are automatically propagated throughout the entire test project, thereby reducing the amount of maintenance work or corrections to a test project. At the same time, some MBT tools require advanced programming skills or familiarity with fields of mathematics like combinatorial logic. Therefore, when considering whether or not to use MBT, it is useful to ask questions such as:
While automated test case creation might sound useful, and indeed can be very useful &#; especially when large sets of test cases need to be adjusted or adapted to other projects &#; it might not be the optimal choice for a test team&#;s first project. For example, writing test cases manually is highly recommended for getting familiar with newly-adopted test software as well as for exploratory testing.
As mentioned in the previous question, test output appears in forms, such as logs and reports. Depending on the automation tool, test output varies from one product to the next. Some software include automated reporting as part of the software or as an extension. On the other hand, some test tools provide limited data about the results, such as decentralized logs that the user must parse themselves.
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