User Guide > Analyzing Test Data with Data Coverage > Data Coverage Task Columns
  

Data Coverage Task Columns

Create a column in a data coverage task to analyze combinations of data values in a data set for density of data coverage. You can also create columns to use as filters in the data coverage analysis.
You can analyze and plot the data coverage in different ways based on the kind of data in the data set. You can use individual column values in the analysis or assign the data to ranges that you create. You can create mapping values and map the data values to mapping values.
You cannot use source columns with binary data type in a data coverage task.
You can create columns to use the data in the following ways:
Use as is
Use individual data values in the analysis to plot the data coverage of distinct values. Individual values are plotted in the graph in the data coverage task. Use data as is when you have a small number of distinct values in the column and you want to view data coverage for individual values. For example, low cardinality columns.
Range
Create ranges of values and analyze the data based on these ranges. For example, a table on employee information includes a Salary column. You want to analyze the data coverage for different salary values across different locations. You can create ranges for the salary values. The data coverage analysis indicates the data density for different salary ranges across locations.
You can use ranges for numeric and date data types
Mapping
Create mapping values to analyze data in groups. Map each of the data values to a mapping value. You can then use the mapping value in the analysis to plot data density across groups of values. For example, a test case requires data in a few regions. You therefore want to analyze the distribution of data across regions. The data contains a column States. You can create mapping values such as East, West, North, South, and assign states to a mapping value. You can then analyze the data distribution across regions.
You can map data values to a single mapping value. You can map multiple data values to the same mapping value.

Data Type Exceptions

You cannot create data coverage columns with source data columns that contain certain data types.
The following table lists the data types that you cannot use to create data coverage columns:
Data Coverage Column Type
Data Type
Use As Is
You cannot use the following data types to create data coverage columns that use data as is:
  • - Date
  • - Real
  • - Double
  • - Precision
  • - Decimal
  • - Decimal (p,s)
  • - Float
  • - Binary_float
  • - Binary_double
Range
String
Mapping
You cannot use the following data types to create data coverage columns that use mappings:
  • - Date
  • - Real
  • - Double
  • - Precision
  • - Decimal
  • - Decimal (p,s)
  • - Float
  • - Binary_float
  • - Binary_double
You cannot include the following data types in data coverage tasks:

Creating a Data Coverage Column Using Data As Is

Create a column using data values as is to analyze the data coverage for individual data values.
Review the list of data type exceptions before you create a data coverage column.
    1. Open the data coverage page.
    2. To add a column that you want to plot on the graph, click the Add button on the Columns pane.
    The Add Column window opens.
    3. From the list of tables, select the table that contains the column.
    4. From the list of columns, select the source data column to which this column refers.
    The column data appears in the Data Preview pane.
    5. Enter an alias name for the column.
    Column aliases must be unique within a task.
    6. Select the Use as is type.
    The data from the preview tab is added to the Values tab.
    7. Optional. You can edit the data in the Values tab. Click the Reset button to replace the edited data with data from the preview pane. Click the Add or Remove button to add or delete individual values.
    8. Click OK.

Creating a Data Coverage Column Using Data Ranges

Create a column using ranges to analyze the data coverage of data values across specific ranges. You can use ranges to analyze data that is in numeric or date data type.
Review the list of data type exceptions before you create a data coverage column.
    1. Open the data coverage page.
    2. To add a column that you want to plot on the graph, click the Add button on the Columns pane.
    The Add Column window opens.
    3. From the list of tables, select the table that contains the column.
    4. From the list of columns, select the source data column to which this column refers.
    The column data appears in the Data Preview pane.
    5. Enter an alias name for the column.
    Column aliases must be unique within a task.
    6. Select the Range type.
    7. To create a range, enter the start value of the range in the Start field and the end value of the range in the End field.
    The start value must be less than the end value. There must be no overlap of values in different ranges.
    8. Enter a label for the range in the Label field.
    9. Click the Add button to add another range.
    10. Repeat steps 7 to 9 to create the required number of ranges.

Creating a Data Coverage Column Using Mappings

Create a column using mappings to analyze the data coverage of data values across specific groups of data values.
Review the list of data type exceptions before you create a data coverage column.
    1. Open the data coverage page.
    2. To add a column that you want to plot on the graph, click the Add button on the Columns pane.
    The Add Column window opens.
    3. From the list of tables, select the table that contains the column.
    4. From the list of columns, select the source data column to which this column refers.
    The column data appears in the Data Preview pane.
    5. Enter an alias name for the column.
    Column aliases must be unique within a task.
    6. Select the Mapping type.
    The data from the preview tab is added to the Values tab.
    7. Optional. You can edit the data in the Values tab. Click the Edit button to edit the values. Click the Reset button to replace the edited data with data from the preview pane.
    8. To create a mapping value, click the Edit button in the mapping values panel.
    9. In the Value field, enter a mapping value name and click the Add button to create another value field.
    10. Repeat step 9 to create the required number of mapping values.
    11. Click OK.
    12. In the Add Column window, select a data value in the Column panel and select the mapping value that you want to map it to.
    13. To link the data value to the mapping value, click the Add Link button.
    An arrow appears to indicate the mapping value that the data value is linked to.
    14. Repeat steps 12 and 13 to link each data value to a mapping value.
    15. Click OK.