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Data Models in Test Automation

Posted by Albert Gareev on May 15, 2007 | Categories: Practices

What’s it about?

Automated tests represent a certain test logic as a sequence of steps, manipulating data and interacting with a GUI.
Test data, as in manual testing, could be data we feed into application-under-test (input data), retrieve from application-under-test (actual data, or actual result), and compare with (expected result).
These data have to be stored in some form.

Even if test data presented “as is”, i.e. hard-coded, this is a primitive data model.

Data Models by the level of organization

Chaotic data model

Unorganized, scattered across the code data. Typically it’s a mix of hard-codings, constants and variables.

  • No formal naming convention
  • No or unorganized initialization and clean-up routines
  • No isolation and protection mechanisms
  • Extremely hard to maintain

 

Linear data model

A set of variables and constants is used to store test data. All names are declared beforehand, typically, at the top of the script.

  • Might follow naming convention (but often doesn’t)
  • Might have informal initialization and clean-up routines
  • No isolation and protection mechanisms
  • Hard to maintain

 

Array-based data model

An array, set of arrays, or a table-type object is used to store test data. Data value names are indexed.

  • Typically has naming convention
  • Has initialization and clean-up routines
  • Might have isolation and protection mechanisms
  • Average maintenance effort

 

Table-based data model (Data Table)

A set of tables as a single object. Example: DataTable object in QTP.

  • Enforced naming convention (column names)
  • Standardized initialization and clean-up routines
  • Supports data isolation and protection
  • Low maintenance cost

 

Tree-based data model  (Data Space)

Data structure (object class) with an access based on associative programming methods. Supports tables, but also supports loops and branching within a single data row.

  • Enforced naming convention (indexes)
  • Standardized, built-in, automatic initialization and clean-up routines
  • Supports data isolation and protection
  • Supports run-time data branching, transformation, import, and export
  • Lowest maintenance cost

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Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported
This work by Albert Gareev is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported.