Test design with model-based testing
Model-based testing sounds like the holy grail of quality assurance: efficiency, automation and maximum precision. But in practice, you stumble...
The use of AI in test automation opens up exciting opportunities to increase efficiency and make development processes more flexible. By using generative AI, not only can test cases be created automatically, but functional code can also be generated - including the conversion of drawn sketches into HTML code. At the same time, sound documentation and the avoidance of technical debt are crucial to creating sustainable systems. Companies benefit from valuable approaches on how AI tools can be used safely and purposefully to ensure long-term success and promote innovation in the field of software development.
In this episode, I spoke with Matthias Zax about the exciting world of test automation and the use of AI. Matthias explained how he uses generative AI to create test cases and generate code and shared his experiences and the challenges involved. A highlight was his story about turning a drawn sketch into working HTML code. We talked about the importance of documentation and the risks of technical debt. Matthias also gave valuable tips on how companies can use AI tools safely and efficiently. It was a fascinating conversation that offered many insights into the future of test automation.
“I think most of us thought, now I can finally generate my unit tests. That’s the worst thing you can do.” - Matthias Zax
Matthias Zax is a dedicated Agile Engineering Coach at Raiffeisen Bank International AG (RBI), where he drives successful digital transformations through agile methodologies. With a deep-rooted passion for software development, Matthias is a developerByHeart who has been honing his skills in software testing and test automation in the DevOps environment since 2018. Matthias is a driving force behind the RBI Test Automation Community of Practice, as well as for continuous learning and innovation.
The possibilities and potential of generative AI are a promising field in the area of software testing. Particularly in the context of test automation and test case design, this technology opens up innovative approaches that can increase the efficiency and quality of tests. Recommended by industry experts, it is clear that practical experience and findings provide key insights into how generative AI can be used productively.
Getting started with generative AI often involves practical testing. Test case design and automated code generation show that the application possibilities are almost unlimited. However, the technology also has limitations that are particularly relevant for experienced developers and test automation specialists with a close connection to the source code. By making intensive use of language models in everyday work, developers can increase their efficiency and complete repetitive tasks more quickly.
A key application example for generative AI is the automation of existing manual test cases in projects. Language models can help to create automated tests or optimize existing test cases. The feedback from the AI on the automatability of certain test cases is particularly valuable, which lowers the entry barrier for testers without in-depth programming knowledge. Generative AI therefore helps to reduce testing effort and improve quality assurance in agile development cycles.
The integration of generative AI also brings challenges, particularly in terms of data protection and data security. In data-intensive industries, such as the financial sector, the protection of sensitive information is essential. One solution is to use internal language models that run exclusively on the company’s servers and therefore do not transmit any data to the outside world. This allows companies to ensure that the use of AI-based tools complies with data protection regulations.
The use of generative AI in software testing could help to reduce technical debt and increase the quality of software development in the long term. A higher degree of automation allows developers to focus on more complex tasks, while routine processes are efficiently covered by AI. In the future, generative AI could therefore play a key role in accelerating software development and improving code quality in the long term.
Generative AI can generate test cases automatically, which increases test coverage and reduces the manual creation of tests. AI models analyze existing data and patterns in the application and use them to create tests that cover possible errors and edge cases.
Generative AI offers faster test cycles, better test coverage and the ability to automatically adapt tests to new software versions. This saves human resources and creates a more robust test strategy that can also find rare errors.
Generative AI can automate many repetitive tests, but manual testing remains important for exploratory and UX-related test cases. The combination of AI-based and manual tests leads to a more comprehensive test strategy.
For test automation, models such as GPT, BERT and T5 are used to create and analyze natural language test cases. Each of these models has different strengths, from text generation to semantic analysis.
NLP enables generative AI models to understand requirements in natural language and translate them into tests. This facilitates the creation and adaptation of tests without requiring in-depth technical knowledge.
Generative AI can automatically generate error reports and analyze the cause of the error. This analysis is accelerated by pattern recognition and data processing, which helps developers to react more quickly to problems.
Yes, generative AI is ideal for regression testing as it can dynamically generate and adapt tests as the application changes. This reduces the need to manually update existing tests after each change.
Challenges include the complexity of the implementation, the quality of the training data and the need for a good model. In addition, the management and debugging of AI tests can be demanding.
Generative AI improves the quality of tests by generating consistent, comprehensive and intelligent test cases. This leads to higher test coverage and better detection of edge cases, which improves overall software quality.
There are numerous tools that use generative AI in test automation, such as Applitools, Testim and Tricentis. These tools offer features for test case generation, analysis and adaptation to new releases.
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