The further development of software testing is increasingly characterized by artificial intelligence (AI). Two key areas can be distinguished: the testing of AI-based systems and the use of AI as a tool in the testing process. Many companies are already working on integrating AI technologies into their products and processes. At the same time, agility plays a decisive role in projects, with Scrum being one of the leading methods. The combination of AI and agile approaches opens up exciting possibilities for the future of software testing.
In this podcast episode, I had the pleasure of speaking with Tilo Linz. We talked about how software testing is evolving, especially with regards to Artificial Intelligence (AI). Tilo explained that there are two main areas: testing AI-based systems and testing with AI as a tool. He believes that most companies are already working on integrating AI into their products. Another highlight was the discussion about agility in projects, where Scrum was highlighted as the dominant method. Finally, we talked about the future and the exciting possibilities that AI and agile methods offer for software testing.
“Almost every serious software development company is looking at which functions in their product can be improved using AI.” - Tilo Linz
Tilo Linz is CEO and co-founder of imbus AG, a leading solution provider for software quality and software testing and has been active in the field of software quality for more than 25 years. As founder and chairman of the German Testing Board e.V. and founding member of ASQF e.V. and ISTQB, he has been instrumental in shaping and advancing education and training in this field at national and international level. Tilo Linz is the author of “Testen in Scrum-Projekten” and co-author of “Basiswissen Softwaretest”
Artificial intelligence (AI) is playing an increasingly important role in software testing. A distinction can be made between two main areas: the testing of AI-based systems and the use of AI as a tool for testers. Numerous companies are currently researching how AI can be used to improve products and processes. The use of generative AI systems to support the testing process is particularly noteworthy. These can, for example, help to generate test data or create test cases automatically, which saves considerable time and resources.
Testing AI systems poses specific challenges. Many of these systems are not yet in a production-ready state. Companies prepare for the use of such technologies by developing prototypes and conducting experiments. Dealing with the specific types of errors that can occur in AI systems is particularly complex. This complexity requires specialized testing methods and a high level of expertise to ensure the quality and reliability of the systems.
Another area of application for AI is as a support tool for testers. Studies show that AI is primarily used in software development in the areas of programming and testing. For example, AI algorithms can be used to automate security tests or develop targeted test procedures. However, these tools mainly act as assistants that supplement human expertise rather than replacing it.
Agile methods are now established in most software projects, with Scrum being the most widely used method. Highly regulated industries such as aviation and medical technology have also introduced agile working methods. This development was made possible by adjustments to the regulations, so that agile methods are also practicable in highly controlled environments. As a result, projects can be implemented more flexibly and efficiently.
The future of software testing is characterized by several exciting developments. In addition to the influence of AI and agile methods, areas such as requirements engineering and scenario-based testing are becoming increasingly important. Automated tools could provide even greater support in the future, which could further improve both the efficiency and quality of testing. Such advances have the potential to fundamentally change software development in the long term and increase the reliability of tested systems.