Data-driven testing
From Wikipedia, the free encyclopedia
 |
The introduction to this article provides insufficient context for those unfamiliar with the subject. Please help improve the article with a good introductory style. |
Contents |
[edit] Introduction
In the testing of software or programs, several methodologies exist in dictating how this testing will be performed. Each of these methods exist because they differ in their effort required to establish initially and then to subsequently maintain. This article aims to explain the data-driven method which is part of the automated testing discipline.
 |
This article is orphaned as few or no other articles link to it. Please help introduce links in articles on related topics. (April 2007) |
[edit] Methodology Overview
Data-driven testing is a methodology used in Test automation where test scripts are executed and verified based on the data values stored in one or more central data sources or databases. These databases can range from datapools, ODBC sources, csv files, Excel files, DAO objects, ADO objects, etc. Data-driven testing is the establishment of several interacting test scripts together with their related data results in a framework used for the methodology. In this framework, variables are used for both input values and output verification values: navigation through the program, reading of the data sources, and logging of test status and information are all coded in the test script. Thus, the logic executed in the script is also dependant on the data values.
This is similar to Keyword-driven testing in that the test case is contained in the data values and not in the test script; the script is just a "driver" or delivery mechanism for the data. Unlike in keyword-driven testing, though, the navigation data isn't contained in the test script. In data-driven testing, only test data is contained in the data source.
