Model-based-testing is an approach where models are used, abstractions of the real world, to improve the efficiency and effectiveness of software testing. Test modeling is more about high-level test design than low-level test coding. Combining Test Data & Expected Results with Real world testing scenarios, scheduling concurrent user simulations and creating flexible execution strategies, this high level approach saves a lot of redundant and repetitive work. Models are used to:

• describe test environments
• describe test strategies
• generate test cases
• enable test execution for software and/or system testing
• measure test design quality
• implement full traceability between system requirements, models, code, and test cases

Implementing the above, model-based test automation approach addresses the key challenges that developers and testers face in their efforts to create better software, faster. These pain points span across the testing lifecycle, from error-prone testing as result of unclear requirements and poor test case design to inefficient, often manual processes that are slowed down by unavailable test data and missing systems components.



The main challenges that model-based testing addresses are:

Incomplete requirements: This results in poorly tested applications being released into production.

Poor test design: this excuse is the general refuge of testers who cannot keep pace with rapid development and deployment cycles. A new paradigm of communication and documentation is required to create better, more robust and flexible tests.

Lack of test data: software testing without data is fast and easy, but it does not result in high quality applications. Data sets often get stored and lost in CSV files, compromising the quality and robustness of tests.

Limited or ineffective test automation: automation is not easy, but it is a must-have element of any modern testing program.

Outdated infrastructure: the days of waiting for test servers or licenses to become free should be over. These constraints impede productivity, quality, innovation and, ultimately, the willingness to break the silos.

Damaged reputations: Traditional testing approaches have been repeatedly failing  ruining companies’ reputations.

What Model-based testing offers is the reduction of  manual effort of designing test cases and ensures quality is embedded early in the application lifecycle. Automating the creation of both manual test scripts and automated test scripts using a model not only saves effort and thereby cost, but increases coverage and also significantly reduces the time-to-market.

Validata’s Model-based Test Automation approach goes beyond traditional script-based methods which can only cover as much as 20% of your application. Validata uses “Generic modeling” principles to build the application knowledge within the tool, by capturing all the application knowledge, data types and relations between data in the dynamic changes (any changes to the application) and updates itself, through various application components, GUI (graphical user interfaces), images, files etc.

It eliminates the additional costs for automation script maintenance which are a resource-intensive and expensive activity every time an application undergoes a change. Clients would now be able to adopt test automation as a strategic and long-term solution and benefit from lower costs and faster time to market.