AI revolution in test automation

The podcast episode is about test automation and the use of machine learning to find errors faster and integrate tools. Unsupervised learning and test-based modeling enable more efficient test processes. Augmented testing combines human knowledge and automation for more accurate results. The future of test management will be discussed in terms of the role of AI in test automation and further optimization.

“I come in in the morning and no longer find 200 failed tests, but I find 7, maybe 8 causes” - Thomas Steirer

Thomas is a test automation architect from Vienna with over 15 years of experience in this field. He has developed numerous automation frameworks and solutions in a wide range of industries and technologies. His focus is on building scalable and sustainable solutions that are primarily designed to deliver valuable information. In his work at Nagarro, he supports customers in the introduction and optimization of test automation, teaches at universities in Austria and is co-author of the book “Basiswissen Testautomatisierung” (Basic Knowledge of Test Automation)

Highlights of this episode:

• Thomas and I have known each other since our first test projects in Vienna
• Thomas is CTO at Nagaro and an expert in artificial intelligence and test automation
• We talk about a research project that investigates how machine learning and AI can be used to improve test automation structures
• It’s about extracting new knowledge from existing information and identifying causes of errors
• We discuss how to write new test cases and use visualizations to increase test quality
• Thomas talks about a research project that involves visualizing test portfolios and gaining valuable insights from them
• The importance of making life easier for testers by minimizing repetitive tasks is emphasized
• Thomas talks about the future of testing and how machine learning can help increase test depth and coverage
• We discuss the role of testers in an AI-assisted testing landscape and conclude that human testers are still essential

How artificial intelligence is redefining test automation

The AI revolution in test automation is fundamentally changing the way we test software. By using AI and machine learning, we can optimize and automate testing processes, resulting in more efficient and effective testing. These technologies not only enable us to identify and fix errors faster, but also to generate new test scenarios and expand test coverage.

The convergence of AI and test automation

In recent years, the landscape of software development has changed dramatically. A driving force behind this transformation is the integration of artificial intelligence (AI) into test automation. As I discussed in this episode with Thomas Steirer, CTO at Nagarro, we are at the beginning of a revolution that has the potential to fundamentally change the way we approach software testing. The integration of AI into test processes not only makes it possible to optimize existing test scenarios, but also to open up new ways of identifying and eliminating errors.

A beer garden conversation

The idea for the research project arose from a seemingly banal moment - a conversation between colleagues in a beer garden. But as is so often the case, it is precisely these unexpected moments that lay the foundation for innovation. Thomas described this moment to me as a point of self-reflection and creative exchange, which ultimately led to the decision to use machine learning (ML) to optimize test automation processes. This step marked the beginning of our journey together into the world of AI-supported test automation.

From theory to practice: initial successes

Our research project focused on applying machine learning methods to existing test automation structures. A key objective was to extract new knowledge from existing data and identify the causes of errors more efficiently. Thomas explained to me how, by analyzing log files and using algorithms, we were not only able to identify the causes of errors more quickly, but also generate initial suggestions for new test cases. The success of this approach confirmed our conviction: AI can add significant value to test automation.

Visual analysis and model-based testing

Another breakthrough in our project was the development of visual analysis tools and model-based testing approaches. By merging all technical test steps in a graph, we were able to create a visual model of the application structure. This model not only enabled us to quickly identify redundancies and error hotspots, but also to derive new test scenarios - a milestone in our research into the integration of AI into test automation.

in future Automated generation of test cases

Looking back at what has been achieved and ahead to what is still possible, it seems clear that the future of test automation lies in the further integration of artificial intelligence. Thomas outlined exciting prospects such as the automated generation of test cases based on AI-generated assumptions or even explorative tests controlled by AI. These developments promise an unprecedented increase in efficiency in the testing process.

Man vs. machine in testing

Finally, of course, there was the question of the role of humans in the age of AI-supported test automation. Despite all the progress, Thomas and I agreed that the human factor remains irreplaceable. It’s not just technical know-how, but also an understanding of context and nuance that makes for high-quality testing. The integration of AI offers enormous support possibilities and can make our work easier - but in the end, it is still the human factor that determines quality.