Richard Seidl
Business Analyst with a quality perspective
Integrating quality into the software development process should be a priority from the outset. The experience gained in testing can be a valuable...
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.
Podcast episode on software testing trends
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”
Highlights of the Episode
- Trends in software testing show an increasing importance of artificial intelligence (AI), both for the testing of AI-based systems and for the use of AI tools in the testing process.
- Many companies are already developing AI prototypes and integrating them into their products.
- Agility continues to play a central role in projects. Scrum dominates, followed by Kanban and some existing V-model projects.
- Scenario-based testing is becoming increasingly relevant, particularly in the area of autonomous vehicles.
- The future development of test automation holds a great deal of potential and remains a key focus area.
The future of software testing
The importance of artificial intelligence (AI) in software testing
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.
Challenges in the testing of AI systems
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.
AI as a tool in the testing process
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.
Agility in software development
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
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.
Frequently asked questions about trends in testing
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What role does artificial intelligence (AI) play in software testing?
Artificial intelligence (AI) is revolutionizing software testing by automating and optimizing test processes. By using machine learning, tests can be designed more efficiently and errors can be detected at an early stage. AI-based tools analyze large amounts of data, identify patterns and predict potential problems, which improves test coverage and increases the quality of the software.
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What is meant by shift-left and shift-right testing?
Shift-left testing means starting testing activities earlier in the development process in order to detect and correct errors at an early stage. Shift-right testing, on the other hand, focuses on testing after the software has been deployed, especially in production environments, in order to monitor and optimize user experience and performance. Both approaches aim to increase software quality and shorten development time.
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How does cloud technology influence software testing?
Cloud-based testing enables flexibility and scalability as test environments can be deployed and customized quickly. Teams can access the same resources from different locations, facilitating collaboration. It also reduces the need for physical infrastructure, lowers costs and supports parallel test executions, increasing the efficiency of the testing process.
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What challenges do microservices and containers bring to testing?
Microservices and container technologies such as Docker and Kubernetes require new test strategies, especially for integration tests and end-to-end tests. The complexity of distributed systems is increasing, which increases the need for service virtualization and special test tools to ensure that all components work together seamlessly and overall functionality is guaranteed.
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Why is the testing of IoT devices gaining in importance?
The increasing proliferation of IoT devices is placing new demands on software testing, particularly in terms of security, connectivity and performance. Tests must take real-time data, latency and device interoperability into account to ensure that IoT systems function reliably and securely and meet user expectations.
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What significance do security tests have in the current software development process?
With the growing awareness of data protection and cybersecurity threats, security testing has become essential. They help to identify and fix vulnerabilities before software goes into production. Penetration tests and vulnerability scanning ensure that applications comply with current security standards and are protected against potential attacks.
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What is exploratory testing and why does it remain relevant?
Exploratory testing is an approach in which testers examine the software without predetermined scripts to discover unexpected errors. Despite advancing automation, this approach remains relevant as it uses human intuition and creativity to identify problems that automated tests may miss, further improving software quality.
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How important is test data management in software testing?
Effective test data management ensures that realistic and data protection-compliant test data is available. This is particularly important in the context of data protection regulations such as the GDPR. Tools for test data anonymization and generation are crucial to ensure the integrity of the tests while complying with legal requirements.
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How do Agile and DevOps influence software testing?
Agile methods and DevOps practices seamlessly integrate testing into the development and deployment process. This promotes continuous testing and fast feedback loops, improving software quality and shortening time-to-market. Testers work more closely with developers, resulting in more efficient error detection and correction.
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What is codeless test automation?
Codeless test automation makes it possible to create automated tests without in-depth programming knowledge. Through visual interfaces or keyword-driven approaches, even non-developers can design and execute tests. This speeds up the testing process, reduces technical barriers and encourages team collaboration by enabling more stakeholders to contribute to quality assurance.
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