If you are developing a complex software application with many functional/business flows and external interfaces, testing the application can be challenging. As there are many flows and interfaces due to the complex structure and functionality, the chances of missing a small step in the testing process are high. Model-based testing is an efficient way to review such complex applications effectively in a shorter time cycle.
Model-based test automation provides a quicker, more robust, cost-effective, and scalable way to test software. In any DevOps environment, inefficiencies are unaffordable, however, with the smallest number of test cases and maximum test coverage, the model-based approach saves a lot of time. This approach uses model creation as an essential part of the software development life cycle instead of test script development. Therefore, the team needs to develop a model that depicts the real-world experience of the testable product.
Model-based test automation is a modern software testing approach that uses application models to generate test cases automatically. It uses a secondary implementation of the software build that is lightweight (the model). This model is derived from the real-world functions and demonstrates the expected results of the system under testing. It is used so that the states are explainable, sharable, reusable and have a precise description about how the system exactly works. While UML sequence diagrams present complex system flows, model-based testing uses mathematical models called transition machines that demonstrate source code and UML diagrams.
Here are different ways of creating models:
To begin modeling an application, let’s take a simple example. Consider a web application that you want to log in to. There are three possible actions that you may take.
Now here, you can come back to the first state from both the second and third states. In the simplest form of a login scenario, these states can be modeled in the transition diagram. Also, there could be multiple permutations and combinations of the flow of the actions taken by the user. Therefore, there can be multiple interconnected models of the application. Automated validation tools are used to evaluate these multiple combinations of states and flows. Moreover, the test cases are generated using the state diagrams.
Script-based test automation is a widely popular standardized method, and it has two main challenges:
Testers and developers need to establish a scripting standard and create and maintain a library of keyword functions. This consumes a lot of time and sometimes becomes a bottleneck within the automation efforts.
2. The additional cost of resources
In script-based test automation, the test cases are essentially developed using technical language. Therefore, the testing efforts are limited to technical experts who understand the nomenclature. This introduces a level of risk as the expertise is restricted to ‘limited technical experts.’
With model-based test automation, the entire system is divided into smaller manageable components. These components further use algorithms to model and recreate the entire system by forming a group of models to generate tests.
Here are additional benefits that model-based test automation offers:
• Reduced testing time
While most time is spent writing and maintaining the model, overall testing time and efforts can be saved with model-based test automation as it does not consume time in the failure analysis stage after the first execution. Also, some model-based tools are capable of finding the shortest test sequence that can cause failure.
• Improved quality of test cases
Reproducing the test cases developed out of manual testing effort is not possible as the quality of the test cases solely depends on the testing engineer’s skills. However, with model-based testing, the automated test generator leverages test cases from the model developed, ensuring that the design process is systematic and can be repeated.
• Test Coverage
The coverage criteria defined before generating test cases can be leveraged to evaluate test progress. With model-based testing, model coverage provides insights into the effectiveness of the testing effort when failures are not revealed through testing.
Traceability shows how relevant the test case is with the model created system requirements and selection criteria. It defines the test case and explains why it was generated. Moreover, it helps optimize the test execution as there are chances that only subsets of the tests are required to be executed in case of the model modifications.
• Requirements changes
Usually, there is a lot of effort required when there is the slightest change in the system requirement. However, in model-based testing, by updating modeled components, all the changes can be covered. As a model is much smaller than the test case, updating it is quicker than updating test cases manually.
Model-based test automation is a robust and cost-efficient approach for enterprises in the long run. While it must be part of the development cycle, setting up the processes, infrastructure, and the complete overhauling of the STLC (Software Testing Life Cycle) can become a challenge.
eInfochips has partnered with Tricentis, which provides one of the best continuous testing platforms that accelerates testing with a script-less, AI-based, no-code approach for end-to-end test automation. eInfochips, leveraging its automation specialists and certified practitioners, provides Tosca and qTest consulting, implementation, and managed services. Consumer electronics, industrial, telecom and medical device customers can create tests quickly with MBTA (Model-based Test Automation) approach, seamlessly integrate testing into agile and DevOps workflows, and improve overall quality.
To know more about our quality engineering services, speak to our experts.
This post was last modified on May 20, 2021 12:32 pm
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