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 of 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 "Tester 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
No-code/low-code approaches are revolutionizing software testing trends by accelerating the development and testing of applications. These platforms also enable non-programmers to quickly create prototypes, which changes testing requirements. Testing is becoming increasingly automated, and new roles are emerging for testers to focus on quality assurance of these rapidly developed solutions. In addition, the simplification of development allows for more frequent releases, which requires a continuous test cycle to ensure product stability and functionality.
Agile methodologies and DevOps practices are shaping current software testing trends by promoting continuous integration and delivery. Testing is integrated earlier in the development process, increasing quality and speed. Automation plays a central role in increasing efficiency and reducing manual errors. In addition, close collaboration between developers and testers promotes faster feedback, leading to quicker adaptation to customer needs. Overall, these approaches lead to more flexible and efficient testing procedures that optimize the software development cycle.
Test data management is increasingly seen as an important trend in software testing, as it significantly increases the quality and efficiency of tests. Through the targeted management of test data, companies can simulate realistic scenarios and identify malfunctions more quickly. This practice not only reduces the time required for data creation, but also improves test coverage. In today's dynamic software development, reliable and scalable test data is essential in order to meet the constantly growing requirements. Test data management is therefore a decisive factor in the current software testing trends.
Exploratory testing plays a decisive role in current software testing trends, as it promotes flexibility and creativity. It complements automated test procedures by allowing testers to react intuitively and interactively to unforeseen use cases. This method enables a deeper analysis of the user experience and helps to identify errors that remain undetected by standardized tests. By using exploratory testing, software quality can be significantly increased while development teams adapt more quickly to changes.
Security testing is a key component of current software testing trends. In the face of increasing threats from cyber attacks, companies need to ensure that applications are protected from potential vulnerabilities. Security testing helps to identify and fix vulnerabilities at an early stage, which increases user confidence. Automated security testing is increasingly being integrated into continuous integration and deployment processes to respond to security requirements in real time. This ensures both the security and quality of the software.
Software testing trends, such as the testing of IoT devices, are crucial for companies as they guarantee quality, security and user satisfaction. With the increasing connectivity and number of IoT devices, the complexity and risks increase. Effective testing helps to identify errors early and reduce costs while meeting compliance requirements. This enables companies to remain competitive and increase customer confidence. Early and systematic testing of IoT solutions is therefore essential for sustainable business success.
Current software testing trends are strongly influenced by cloud technology, especially through the introduction of cloud-based test environments. This enables scalable, more flexible testing processes and faster deployments. Automation and Continuous Integration/Continuous Deployment (CI/CD) are gaining importance as teams can react faster to errors. Cloud services also promote collaborative working, which increases the efficiency and quality of tests. Security tests are also being stepped up to protect cloud data. Overall, the cloud is revolutionizing the software testing process by increasing agility and efficiency.
Microservices and containers lead to challenges in software testing such as complex integration tests and higher dependencies. The distribution of functionalities makes it difficult to validate the overall behavior. Automated tests and continuous testing are essential software testing trends to support rapid deployments and ensure quality. In addition, the dynamic nature of container environments requires flexible test strategies and monitoring to ensure performance and security. These trends help to keep track of the many changes and releases.
Shift-left testing refers to the integration of tests early in the development process to detect errors at an early stage and improve quality. Shift-right testing, on the other hand, means performing tests after deployment to analyze user behavior and application performance in the real environment. Both approaches are important software testing trends that help to increase software quality and promote more efficient development cycles. By applying testing both left and right in the process, overall software development is optimized.
Artificial intelligence (AI) is revolutionizing software testing trends by automating testing processes, identifying errors faster and improving test coverage. AI-supported tools analyze code and user behavior to specifically identify risks and make tests more efficient. It also enables predictive analytics to predict future problems. Machine learning allows tests to be adapted dynamically, which increases the quality of the software and shortens development cycles. Overall, AI significantly increases the efficiency and effectiveness of software testing.