Model-Based Test Generation: A Proven Technique for Scalable and Rigorous Mobile Testing

As the use of mobile applications continues to grow rapidly, the need for effective and efficient mobile testing has become increasingly important. Scalable mobile testing requires a rigorous approach that can keep up with the rapid pace of development and deployment. This is where model-based test generation comes in – it offers a proven technique for implementing the principles of mobile testing at scale and automating the creation of rigorous and efficient tests.

In this article, we will explain what model-based test generation is and how it can significantly improve your mobile testing efforts. We will explore the benefits of using visual flowcharts, the importance of reusability, and the power of automating test steps. We will also touch on the limitations of mobile test automation and the importance of not testing exhaustively.

Explanation of Model-Based Test Generation

Model-based test generation is a testing technique that uses visual flowcharts to map out the logic of each mobile component. These flowcharts are created collaboratively by the development and testing teams and can be automatically generated into targeted tests and data for mobile. This approach offers a more systematic and rigorous way to test mobile applications and ensures that all aspects of the application are thoroughly tested.

Visual flowcharts

Scalable test generation begins with creating visual flowcharts that represent your mobile application logic. These charts capture the behavior of the mobile application and are used to create a model of the system being tested. These models can then be executed, and the generated tests can be used to validate the correctness of the system under test.

Reusability of Models for Fast Test Authoring

Each model is reusable, enabling lightning-fast test authoring for end-to-end scenarios. By using models, you can create an inventory of tests that can be reused over time as your mobile application evolves. This approach not only saves time and effort, but also ensures that testing is consistent and thorough.

Automating test steps using reusable actions

Automating test steps is as quick and easy as dragging-and-dropping reusable actions from central repositories. Test Modeller comes equipped with a library of reusable mobile automation actions, while additional code can be synchronized from scripted frameworks. This means that automating tests can be done with minimal effort, and the test coverage is comprehensive.

Access to a library of reusable mobile automation actions

Test Modeller is equipped with a library of reusable mobile automation actions that can be used to quickly and easily automate tests. These actions can be assembled into larger test flows, and the use of data sources means that tests can be executed with different inputs or data sets. This makes the testing process more scalable and efficient.

The Importance of Not Testing Exhaustively in Mobile Testing

With the combinatorial explosion created by mobile testing, you will rarely want to test exhaustively. While it may seem tempting to test every possible scenario, it’s not always practical or necessary. By using models, you can prioritize which tests to run and which ones can be deferred or deprioritized. This means that the testing effort can be focused on the most critical aspects of the application.

Refactoring of generated test cases and scripts

Automated test generation refactors previously generated test cases and scripts, which are all traceable back to the flowcharts. This approach ensures that the generated tests are of high quality and maintainability, making it easy to identify and fix issues when they occur.

The Limitations of Mobile Test Automation

Mobile test automation cannot solely focus on creating more tests faster. There are intrinsic limitations to the speed of test creation, and some tests require a human touch. Additionally, there is the question of which devices to test on and how many devices to test on. Some tests may not be replicable on all devices, which can limit the test coverage.

Model-based test generation offers a proven technique for generating rigorous tests at scale while automating the prioritization and creation of automated tests. By turning visual flowcharts into automated tests, you can create a repeatable, consistent, and rigorous testing process that is scalable and efficient. The accessibility of reusable automation actions and the maintenance of tests through refactoring ensure that the testing process is of high quality and maintainability. If you are looking to scale up your mobile testing efforts, then model-based test generation is an approach worth exploring.

Explore more

Agentic AI Redefines the Software Development Lifecycle

The quiet hum of servers executing tasks once performed by entire teams of developers now underpins the modern software engineering landscape, signaling a fundamental and irreversible shift in how digital products are conceived and built. The emergence of Agentic AI Workflows represents a significant advancement in the software development sector, moving far beyond the simple code-completion tools of the past.

Is AI Creating a Hidden DevOps Crisis?

The sophisticated artificial intelligence that powers real-time recommendations and autonomous systems is placing an unprecedented strain on the very DevOps foundations built to support it, revealing a silent but escalating crisis. As organizations race to deploy increasingly complex AI and machine learning models, they are discovering that the conventional, component-focused practices that served them well in the past are fundamentally

Agentic AI in Banking – Review

The vast majority of a bank’s operational costs are hidden within complex, multi-step workflows that have long resisted traditional automation efforts, a challenge now being met by a new generation of intelligent systems. Agentic and multiagent Artificial Intelligence represent a significant advancement in the banking sector, poised to fundamentally reshape operations. This review will explore the evolution of this technology,

Cooling Job Market Requires a New Talent Strategy

The once-frenzied rhythm of the American job market has slowed to a quiet, steady hum, signaling a profound and lasting transformation that demands an entirely new approach to organizational leadership and talent management. For human resources leaders accustomed to the high-stakes war for talent, the current landscape presents a different, more subtle challenge. The cooldown is not a momentary pause

What If You Hired for Potential, Not Pedigree?

In an increasingly dynamic business landscape, the long-standing practice of using traditional credentials like university degrees and linear career histories as primary hiring benchmarks is proving to be a fundamentally flawed predictor of job success. A more powerful and predictive model is rapidly gaining momentum, one that shifts the focus from a candidate’s past pedigree to their present capabilities and