Updated April 14, 2023
Introduction to Model-Based Testing
In today’s scenario when we test a software product and capture the test specifications, understanding the criteria and having the best test coverage, we, who are in the software world needs to change our approach in gathering, analyzing, and constructing the product requirements. As a result of these recent advancements stated above, we have entered a world of model-based testing which allows the software testing team to leverage the power of test automation more thoroughly. The main intention of automation in testing is not only to have faster and efficient testing but also streamlining the workflow.
What is Model-Based Testing?
Model-based testing is theoretically defined as a software testing technique, where the test cases to be executed are taken from a model which covers the entire functional aspect of the system which is under the test. In other words, we use a model for describing the test environment and test strategy along with generation of test cases, executing those test cases and identifying the test design quality.
The process which we follow from the model, which are used for generation of test cases automatically, is as follows:
- Models are created to capture the behavior of the system that is being tested.
- Interpretation of behavior for development of manual testing scripts are done using tools like fMBT, Modbat.
- The scripts are then generated automatically by the tool.
In industry nowadays, model-based testing has become an integral part of the product design starting from the phase of requirement specification. With the usage of this testing technique, the developers and testers can now heavily focus only on the models that are getting created for covering the system requirements only and build a testable application at the start. And later by using model-based techniques the team can develop more tests using different algorithms.
In a nutshell, model-based techniques when used with popular automation frameworks like Selenium and many such others would help the team as manual and automated scripts would be build and, hence increasing the coverage of the tests.
Examples of Model-Based Testing
Given below are the examples mentioned:
One more thing we need to keep in mind for MBT is, there are numerous models available each describing the aspect of system it was designed for.
Example #1: Data Flow
As a part of the MBT, in this case we characterize the data flow and examine the coverage criteria for the sequence of events related to status of variables or data objects. The portion of testing focusses on the point from which variables receive the values till the point where these values are used.
Example #2: Control Flow
In this type of testing the program’s control flow is use as a model for carrying out structural testing of the software. In this genre the control flow diagram is considered as the key element for testing.
Example #3: Dependency Graphs
In this testing technique, the dependency graph is prepared based on the scenario we are testing. This graph represents the dependencies of several objects with each other.
Example #4: Decision Tables
In this type of example the results are compared head to head i.e. what the actual result is vs what the expected result was. Only if the actual result match to the expected result the test shall pass.
Example #5: State Transition Machines
In this type of testing techniques the state of the machine is checked against some inputs and that notifies the system’s behavior when the state transition takes place. This testing can be used for checking the state for complex and real life systems.
Challenges of Model-Based Testing
The introduction of model-based testing already holds tremendous promise and along with it comes with new challenges which gets introduced as we try to get hold of more coverage in test cases. Once we overcome these challenges, we would be able to extract the maximum benefit from this technique.
- The first of the lot is the intensive training which is required by the developers and testers for understanding the techniques and the working of MBT. The critical part is that the developers are familiar to the coding paradigm, but the requirement of testing knowledge is one of the challenges and needs to be addressed at first. For testers, this is something new over the traditional testing methods.
- One needs to invest time into the architecture and think about the infrastructure required by the team so that the tool can be scalable, be able to handle complex methods, and provide reliable test coverage. Though this would increase the upfront time initially but will get offset later in the cycle by reducing the maintenance time and improving the reliability by increasing the coverage.
- Fine-tuning the MBT tool can be a daunting task, initially, as it runs parallel with creation of model and might require refactoring of the tool. But once you the hang and feel of the technique, it would be super smooth in using MBT.
- The final is the shift in mindset and culture of testing techniques and techniques of development and testing applications, and many are not open for that. But as we say “Change is the only constant”, once we equip ourselves to the MBT way of testing, it will feel more comfortable in the later times.
Conclusion
Though we know about the challenges we face in Model-based techniques, it brings in many advantages to the table. It makes sure that the QA involvement is at the beginning of the discovery phase and hence makes sure that testability of the product design even at the onset of the feature development. As a result of this involvement, one can find flaw in the design and specifications even before the code existence. The testing thoroughness would increase along with coverage and have negligible test-suite maintenance. We can have exhaustive testing possible in MBT. At last, the model-based techniques powered by AI testing and machine learning techniques will bring in huge time and money savings to the business.
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