Datasets

When designing and executing a script, as mentioned in design, the defined actions can make use of data that is stored, and which can be called upon during the execution of our script. This pre-defined data can come from any data file supported by the framework (see connection types).

The use of datasets

A collection of data, which we name dataset, can be read by the intelligent engine during the script execution and can perform certain actions with this given data. The input parameters or data is stored in a datafile(s) (e.g. .db3) and is being refered to during the script design by its label. Next to providing input, the framework also offers the functionality to capture the results or output, generated by performing one or multiple actions (see data logging).

The creation of datasets

The framework is compatible with multiple database files. As used in tutorial, a .db3 file can be created and stored in the folder specified below.

Folder Description
/data Location where data files can be stored

In order for the engine to recognize and read the defined datasets, a dataset connection needs to be established. This can be done during the design of the scripts by using the action type: data.setDatasetConnection

Action Type Description
data.setDatasetConnection Define a dataset connection that can be used in any action

Once the dataset connection is set, it is likely to execute one or more actions to retrieve the data from the earlier declared dataset. A reference to the dataset label is necessary to connect to the peferred dataset. This can be set in the parameters of the action.

The opportunities datasets have to offer

Dataset comparison

Datasets can be compared by using the following action type:

Action Type Description
data.compareDataset Compare two datasets

A comparison might be useful to compare the actual and expected values generated by the action execution. Mapping must be foreseen when comparing specific fields between two datasets. All information about mapping can be found in Mapping.

Dataset logs

The action type data.outputDataset is implemented to provide the user with the opportunity to store and log the values after execution. Thereby, logging and debugging will be become more convenient for the user.

Action Type Description
data.outputDataset Prints a dataset for logging and debugging purposes

Data generation

The framework has included procedures to generate randomized data. Procedures are identified by using the following format: **. Procedures can be either be defined in the scripts or in the dataset as a specific value. This can for example be used for the creation of personal test data. New, customized procedures are easily created and configured in the framework.

Procedure example Description  
    Generates a random based name

To do: –> parametrization –> example