Model based Automation framework



Model-based

Often called an “Intelligent framework”, Model-based frameworks go beyond creating automated tests that are executed by the tool. These frameworks are typically “given” information about the application, and the framework “creates” and executes tests in a semi-intelligent manner. Test automators describe the features of an application, typically through state models which depict the basic actions that may be performed on the application, as well as the broad expected reactions. Armed with this information the framework dynamically implements tests on the application. Model-based Pros ·         Increased coverage overtime – With a minimal amount of scripting, a lot of the application may be tested. The coverage may not necessarily be extremely high per execution, but given the random nature of the testing, the application test coverage will increase over time. ·         Increased application exploration – Test automation is typically created to perform a specific set of test sequences with each test execution. Model-based test automation frameworks contain a certain degree of artificial intelligence, which allows it to perform an almost exploratory type of testing, in a way that is similar to how a manual tester might explore the application. ·         Increased potential for defects discovery – Test automation is normally not meant to uncover a lot of new defects. Test automation is normally used to ensure existing functionality still works. The exploratory nature of Model-based frameworks, however, increase the chances of new defects being uncovered, given the fact that new ground is covered with each automated test run. Model-based Challenges (Con) ·         Requires higher degree of application knowledge – In order to maintain Model-based frameworks, a higher degree of application knowledge is required. This is directly related to the fact that there is less maintenance of automated tests (which is largely a technical activity), and more maintenance of application models that define application behavior. ·         Required technical expertise – The technical skills required to create and maintain Model-based frameworks is relatively high. There are numerous dependencies and relationships that must be understood and maintained, as well as advanced tool components and structures. ·         Increased management support – Management support is probably the most challenging with Model-based frameworks. Mainly because it is a lot simpler to calculate and communicate ROI with other frameworks. Model-based ROI calculations will more than likely be based largely on risks. Risk ROI calculations alone are often very difficult to convey to management as a justification for the time and resources necessary for creating and maintaining the structures, documentation, and personnel (both technical and non-technical).