What do we call an approximation of a testing result that can be used despite incomplete input data?

The term "test estimation" refers to the process of making an approximate calculation or judgment regarding the expected outcomes of a testing effort, even when there is incomplete input data. This is particularly relevant in agile environments, where requirements and conditions often evolve rapidly, and formal documentation may be minimal. Test estimation allows teams to plan their testing activities, allocate resources effectively, and set realistic timelines based on the information available.

In agile contexts, where flexibility and responsiveness to change are crucial, being able to provide a rough estimate helps teams gauge the scope of testing needed and prioritize their efforts. This approximation can inform decisions about what features to test, how much testing can be accomplished in a given timeframe, and what risks might be associated with incomplete or changing requirements.

The other options represent different aspects of testing but do not specifically address the concept of approximation in the context of incomplete data. For instance, a test charter outlines the scope, objectives, and potential risks of testing, while test conditions define the requirements that must be met to ensure the testing is successful. Test data preparation focuses on the creation and management of test data needed for execution. However, none of these options encapsulate the concept of estimating outcomes based on limited inputs as effectively as test estimation does.