Data correlation rules overview

You can use the data correlation rules editor to customize how data is correlated. You can control how references and substitutions are generated in tests, and store these rules so that you do not have to manually correlate data in every test that you record against a particular application.

You create data correlation rule sets in the rules editor. Data correlation rule sets are also known as rules files. Each rule set can contain multiple rule passes, and each rule pass can contain multiple rules. When you re-correlate test data with data correlation rules, each rule set is applied in the order that you specify. Within each rule set, each rule pass is applied in order. Within each rule pass, each rule is applied in order.

You can use data correlation rules to do these tasks:
  • Create a reference, substitution, variable, or datapool column
  • Link a substitution to a reference
  • Rename a reference or substitution
  • Encode a substitution
  • Unlink a substitution from a reference
  • Remove a specific reference, substitution, or variable
  • Remove all references or substitutions

Typically, you create a substitution and then link a reference to the substitution. References are located in the data that the server under test returns, while substitutions are in the data that is sent to the server. To create a substitution and then link a reference to the substitution in the rules editor, see Example: Linking references to substitutions with rules.

Rule sets are hierarchical trees. You can insert child rules, which accept values generated by parent rules as input. To find a particular reference by name, first add a Find a reference rule, and then add a child Reference name rule. In the rules editor, you can also combine rules by using And and Or and Not rules.