QA 2.0: The Era of Smart Testing with AI-Human Integration
In QA 2.0, the marriage of AI and human intelligence is not a full stop but a mere prologue — an introductory chapter to the infinite pages of a Smart Testing story. This is the birth of a tale where testing grows, transforms and adopts.
Quote by Chamila Ambahera
This is the first article of the QA 2.0 series where we are going to discuss utilizing AI tools to simplify tedious testing tasks.
Check the QA2.0 Index page to navigate other QA2.0 stories
The way quality assurance in the software development field is changing as we speak. QA 2.0: A dynamic testing environment enabled by the combination of artificial intelligence and human intelligence, going beyond the conventional ways of doing QA.
The AI Revolution in QA Automation:
AI has reshaped the QA industry, eliminating monotonous activities, increasing pace, and improving effectiveness. It does not sufficiently address complex user behaviour and real-world conditions. Through QA 2.0, this shortcoming is addressed by using the strengths of AI and the cognitive traits of human testers.
The Human Touch: Unveiling the Nuances:
By using human testers you bring in critical thinking, creativity, and domain knowledge which has value that only AI can’t replace yet. For example, intuition is crucial in exploratory testing which helps to identify challenging problems and unusual cases.
The Framework of Synergy:
The integration framework for QA 2.0. Machines like Testim and Functionize use machine learning to develop autonomous test cases which incorporate human intuition. Through this collaboration, a comprehensive approach is adopted in testing that utilizes the agility of AI as well as the instincts of human assessors.
Dynamic Test Case Generation with Functionize:
ALP technology by Functionize employs historical data and provides human insights for developing test cases. As such, Functionize’s AI algorithms adjust to this to incorporate complete test coverage that adapts alongside the application.
Cognitive Test Execution with Testim:
Further, Testim enhances AI-driven test execution by empowering human testers to guide exploratory testing. Testim uses machine learning in order to observe how human testers examine an application. Such an approach will help identify soft issues and corner cases that the team would not necessarily see on their own.
Real-Time Adaptive Learning with Applitools:
This company uses Visual AI to do automated visual testing. Applitools learns from the visual validation patterns that human testers go through as they interact with an application. This ensures that the visual test is an ongoing process that incorporates the learning of the input by both man and machine.
Write UI tests faster with testRegor:
This company provides a UI that helps us to write test cases in plain English. Also, it generates some tests for you. You can write your test case just in a few steps. Also, they provide an execution engine where you can execute the tests.
Self-healing Selenium Tests:
Say goodbye to flaky Selenium tests. Many companies like Healenium, Parasoft, etc provide solutions to improve the test script stability. If the test fails it will analyse the test for you.
Case Studies: Real-World Success Stories:
The adoption of QA 2.0 has been highly successful by many renowned organizations. For example, according to the Cambridge Centre for Alternative Finance, 90% of Fintech companies already use AI, leading to faster time-to-market and customer satisfaction.
Challenges and Considerations:
Although QA 2.0 holds unbeatable advantages, there are challenges like the balance between AI and human input and bias training data that should be taken into account. The two tools such as mabl and Test.ai are trying to solve these challenges to ensure that there are effective collaborations.
Future Outlook:
With QA 2.0, the future of QA looks promising. With time, smart testing will also evolve and include personalization and adaptation in the testing strategies coupled with integration with DevOps practices as technology advances.
Conclusion:
However, QA 2.0 represents a turning point in software testing, where the best of AI and human intelligence come together. This is an age of smart testing that organizations seeking to remain competitive should embrace in software development.
We will discuss further into above points in the next article.
References:
“Sounds exciting? Follow me on Medium to stay connected and dive deeper into the journey of QA2.0.”
