IT transformation has become a significant area of consideration for organizations as technology has become an essential part of almost every industry and trade. Organizations understand the vitality of an agile and faster software ecosystem. Yet, software development teams need help with the burgeoning weight of unknowns and growing complexity within the existing IT infrastructure.
IT change and automation have become priorities for most organizations these days. One part of this automation is software testing. Testing is a vital part of SDLC and instrumental to;
- Assess the impact of change
- Develop software accurately and iteratively
Around 90% of companies believe automated testing improves testing speed, and this contributes to the reduction in cycle time. However, businesses today require the balance of the exact levels of exploratory testing and judicious automation to ensure that the impact of possible risks of the product/application being released is minimized.
How Good Automation Complex and Difficult Task?
Here are some key challenges QA teams face for desired automation, making it a complex and challenging task.
- Connecting end-to-end flows: Business workflows include several overlapping test steps- sequencing test steps for repeated executions can be fraught with data and timing issues.
- Keeping pace with the requirements changes requires constantly adding/modifying different combinations of UX and data. It makes test script writing slow, repetitive, and time-consuming.
- Tests have to deal with dynamic data, potentially leading to the issues of flaky tests.
- Automation requires large amounts of test data that must be accurate, reliable, and comprehensive. Unfortunately, organizations lack appropriate data, and finding suitable data becomes time-consuming and potentially meaningless.
- Automation requires constant maintenance of the test suite, which constant refactoring may bloat, making it a burden on most QA teams.
What should be done to make a seamless and effective QA automation effortless?
The solution is Model-Based Testing. MBT, where models can be used to represent the desired behavior of a system under test, is one of those processes that can help teams overcome challenges of IT change and accelerate the transformation for organizations.
High-performing teams prefer MBT, as one can easily create application behavior models and visualize test paths well before the systems are available to test. Let’s explore how model-based testing approaches can help achieve quality at speed and as envisioned initially by “Agile” techniques.
- Better collaboration and interaction among different stakeholders facilitated by constant interactions between business users, system designers, developers, and testers results in early, high-coverage test design and the ability to execute simulations and assess system behavior
- Improved understanding and visualization of system design and behavior results in improved test coverage
- Enhanced ability to develop modular elements for efficient test automation
- Better ability for non-technical testers/business analysts to contribute to the design of tests
- This model develops an abstraction layer over the test automation code, allowing test script writing without coding skills.
- Optimised test case coverage using algorithms around risk, minimizing the comparative efforts required for finding and fixing bugs, thereby increasing the number of tests to be conducted.
Conclusion
algoQA is designed on the principles of model-based testing. The cutting-edge testing platform offers versatile solutions designed to meet QA teams’ challenges across organizations. It provides easy-to-use drag-and-drop wizards to model application behavior, accelerates the creation of models through intuitive profiling of the application, and auto-generates test cases and test scripts, with associated test data, based on the model. It requires no scripting and no complex setups. algoQA also supports multi-language scripting and supports Web, Desktop, Mobile, and Hybrid Application testing. Test cases can be generated for UI, API, or Load tests.
The platform saves up to 80% of testing and maintenance costs, enhances coverage, and reduces business risks.
algoQA is pivotal in enabling seamless testing in the inevitable need for IT change. Organizations should adopt this or similar model-based testing tools to accelerate and transform their IT infrastructure for better results.
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