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Xray's roadmap is continuously reviewed and redefined. We update often, depending on the feedback we receive from our clients and internal stakeholders. Our release plan is available in our Jira issue tracker. Feel free to view and to vote on the issues that you would like to see implemented (account registration required). Here you can find a list of features that define our main goals for future releases. This doesn't mean that other, potentially smaller features, won't be implemented as well. |
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At Xray, we are always listening to our customers and stakeholders to improve our product and deliver the best value. Our roadmap reflects our vision and priorities for the upcoming releases. |
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Test cases evolve when requirements change, or improvements are made to the application under test. Although Xray logs test specification changes in the Jira history, it is not easy to revert to a specific test case version. With Test case versioning, you can now create multiple versions of the same test. |
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By defining a saved filter as the source of Tests for the Test Plan, all Tests on the filter will be added automatically into the Test Plan by Xray. For example, you can define a Test Plan with a filter containing the Tests associated with Stories from a Sprint. Your Test Plan will collect all relevant Tests for that Sprint automatically. If a Test Plan is configured with a saved filter, users can't add or remove Tests explicitly from the Test Plan. |
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title | Parameterized Tests and data driven testing |
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Test parameterization is a powerful practice that allows the same test to be executed multiple times with different parameters. Parameters are similar to input values (variables) that can change with each execution.
Parameterized tests in Xray are defined just like any other test with the addition of some parameter names within the specification using the following notation: ${PARAMETER_NAME}. This notation is used to reference parameters within the test steps.
Precondition issues can also be parameterized by including parameter names in the precondition specification.
The parameters, along with their values, are defined within a dataset. A dataset is a collection of data represented with a tabular view where every column of the table represents a particular variable (or parameter), and each row corresponds to a given record (or iteration) of the dataset. The number of rows in the dataset determines the number of iterations to execute.
A dataset can be defined in the following entities/scopes:
- Test (default dataset)
- Test Plan - Test
- Test Execution - Test (Test Run)
The closest dataset to the test run will be the one used to generate the iterations, effectively overriding any dataset defined in higher levels.
All iterations for a given test are executed within the context of the same test run. Each iteration can be expanded, and the steps executed individually. The step parameters will be replaced by the corresponding iteration values. The steps affect the iteration status that, in turn, affects the overall test run status.
Learn more about parameterized tests here.
Our goal is to improve the migration process by allowing extra options that can ensure a successful outcome. We will provide a list of issues that may arise during the process and how to resolve them. We will also optimize the process to achieve the migration in the least amount of time possible, without compromising the quality or integrity of the data. |
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Improve the way defects are created by cloning parent issue data. e.g. if test has component selected, the bug created will inherit that value. |
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This gadget enables you to see, at a glance, the evolution/trend of the status of a group of Tests, taking into account the results of those Tests in certain Test Executions. |
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The main objectives of the improvements are to increase the test coverage, reduce the execution time, and enhance the readability and maintainability of the test library |
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Wish list for future versions
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