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Automatic item generation (AIG), or automated item generation, is a process linking psychometrics with computer programming. It uses a computer algorithm to automatically create test items that are the basic building blocks of a psychological test. The method was first described by John R. Bormuth [1] in the 1960s but was not developed until ...
Test data can be generated by the tester or by a program or function that assists the tester. It can be recorded for reuse or used only once. Test data may be created manually, using data generation tools (often based on randomness), [4] or retrieved from an existing production environment. The data set may consist of synthetic (fake) data, but ...
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
A test case generation tool that can automatically generate JUnit tests. GrandTestAuto [308] GrandTestAuto (GTA) is a platform for the complete automated testing of Java software. Tests can be distributed across a number of machines on a network. GroboUtils [309]
Offline generation of executable tests means that a model-based testing tool generates test cases as computer-readable assets that can be later run automatically; for example, a collection of Python classes that embodies the generated testing logic. Offline generation of manually deployable tests means that a model-based testing tool generates ...
Tools are specifically designed to target some particular test environment, such as Windows and web automation tools, etc. Tools serve as a driving agent for an automation process. However, an automation framework is not a tool to perform a specific task, but rather infrastructure that provides the solution where different tools can do their ...
In most cases, test automation covers continuous changes to minimize manual regression testing. Changes are usually noted by monitoring test log diffs. For example, differences in the number of failures signal probable changes either in AUT or in test code (broken test code base, instabilities) or in both.
Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. It is the combination of automation and ML. [1] AutoML potentially includes every stage from beginning with a raw dataset to building a machine learning model ready for deployment.