With increasing visibility and demand for Artificial Intelligence, it is important for us as experts in software testing to do our part to support sustainable trustworthy and secure AI development. With our trainings and workshops, we would like to help you understand important aspects and comprehend test methods. However, we are also happy to formulate and implement tests for you in-house. Which test methodology we use for your AI depends on the type of product. Since artificial intelligence involves complex systems that differ depending on the product, there is no generic test method. Moreover, it may well be that a combination of tests is applied to test your AI. For example, well-known techniques such as the white-box method and the black-box method can be combined here. But we also use more modern methods such as metamorphic testing. It is very important to us that our customers can have confidence in their products. Because even in the technical world of software and AI, there are certain requirements and standards that are regulated by law and must be met accordingly. For this reason, we not only support you in the execution of the tests, but also advise you extensively on the procedures and which is best suited for your project. We are your experts for planning, specification and execution of tests for AI-based.

Contact us and get more information about our approach to AI quality assurance. imbus is your partner for software testing and quality assurance. We test your AI so that you can develop confidence in your product and ensure compliance with all important standards and norms for AI.

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The term Artificial Intelligence also refers to the complex field of research around such systems, which is constantly evolving. It can be divided into different subareas and methodologies. Methodologically, a distinction is made in the field of artificial intelligence between symbolic and subsymbolic AI. Subsymbolic AI is more complex than symbolic AI and thus additionally subdivided into different subareas.

Symbolic AI is the classic approach to the development of artificial intelligence. These systems are part of early research and find their origin in the 1950s. They fall under the category of weak AI because they are designed for specific tasks and also do not draw their knowledge themselves from the data at hand. In fact, symbolic AI cannot function without human intervention because it draws its knowledge from a programmed "expert system" - this is created by humans. By accessing structured data sets, the AI can search for a suitable solution to its task. However, this is now considered outdated and for this reason is sometimes referred to as GOFAI (Good-Old-Fashioned AI) and can be found in speech and text recognition, for example. In short, symbolic AI can only work by embedding human knowledge into a computer program.

Subsymbolic AI can be divided into different subareas, which are all interrelated. Thus, the term Artificial Intelligence is the supercategory of all subfields. Within it are neural networks, machine learning, and deep learning. Here we provide you with an initial overview of quality assurance and software testing procedures in connection with artificial intelligence - Arrange a consultation appointment now! 

For machine learning to work properly, an algorithm must be equipped accordingly. Neural networks are currently the most widely used algorithms for this purpose and are based on the nerve cell connections of the human brain. These networks consist of a large number of layers of nodes that are linked together. The basis for the neural network to learn is repetition. The network learns to classify data correctly and to adjust the weighting of the individual connections between the layers in the event of errors. This happens as long and as often as necessary until certain quality criteria are achieved. These include functional performance metrics such as accuracy, precision or sensitivity, as well as robustness and performance. The neural network is the backbone of deep learning algorithms.

Deep learning is also part of machine learning and primarily uses complex neural networks and large amounts of data. In practice, deep learning is mainly used to understand texts and recognize images, but has a very broad application potential.With the help of deep learning, algorithms can solve complex tasks and problems with sufficient training and often do so faster and more effectively than a human.As the process is very computationally intensive and based on repetition, it can take months for artificial intelligence to find correct solutions or make good decisions.

The historical development of AI began in the 1950s. Researchers such as John McCarthy and Marvin Minsky were concerned with the question of how machines could exhibit intelligent behavior.The first algorithms for symbolic artificial intelligence were developed and tested in these years on the basis of electrical circuits.The Turing test also dates back to this time, which was designed to find out whether a machine has human intelligence or can imitate it. Around 10 years later, progress was also made in the technical world. With the help of computers and transistors, the first steps towards programming an AI were taken in the 1960s. In the following years, the methodology of symbolic AI was expanded. In the history of AI, however, there were often years in which research came to a standstill. In the 1980s, for example, there was a so-called "AI winter". During this time, the symbolic approach reached its limits and it became clear that this methodology was not capable of imitating human intelligence. Accordingly, new methods were sought that could generate human intelligence by machine. The principle of the expert system was particularly influential in research in the 1970s - a well-known example of this is the language assistant ELIZA. In the 1980s and 1990s, neural networks and thus also the topic of machine learning steadily moved to the forefront of research - sub-symbolic AI was researched. In 1997, the first gaming computer based on artificial intelligence gained media attention.Here, too, the technology was not sufficiently advanced for great success, but this changed around 20 years later.With the dawn of the new millennium in 2010, it became possible to make greater use of inexpensive hardware with high storage capacities and computing power and thus collect sufficient data. As a result, research into AI and machine learning could be further expanded.

Artificial intelligence is increasingly being used in companies as a work aid. It is already being used in a wide range of applications to facilitate work processes and relieve employees. After all, AIs can automate certain processes and thus save entrepreneurs quite a bit of time and money. But before you can integrate an AI into your company, you should think about the possible applications and what benefits an AI brings with it. The German Research Center for Artificial Intelligence (DFKI) conducts research that also addresses the benefits of AIs in a wide variety of application areas. According to the DFKI, the following industries are qualified for the application of AIs:

•     Finance
•     Medicine and care
•     Commerce
•     Logistics
•     Industry
•     Agriculture
•     Education

There are big plans for integrating artificial intelligence into companies. Indeed, the general goal is to use new technologies such as AIs to make work processes faster, more effective, less expensive and more secure. For example, an AI can be a great help in evaluating large amounts of data or can also help with fraud detection or system security. Detailed information and research projects on the possibilities of the latest technology in specific work sectors can be found on the DFKI website. Companies can already use artificial intelligence for a wide range of tasks and relieve employees. AI can be a helpful teammate in logistics, customer service or marketing, for example.

imbus TestBench - the smart management system

At imbus, we work every day to test and maintain the software quality of our customers. To this end, we have also developed our own smart test management system, TestBench, which is used by companies that want to develop high-quality products. The management solution helps with manual and automated software tests, among other things.

For over 30 years, imbus has stood for trustworthiness and security in the context of software testing. We have made it our business to accompany and support companies in the development of software with our testing and consulting services. The growing interest in Artificial Intelligence has not gone unnoticed in our company - that is why we offer testing, consulting and also training specifically for companies that are enthusiastic about Artificial Intelligence, or are even developing AI on their own. As the digital landscape is constantly evolving and making tremendous strides, we want to help companies produce trustworthy as well as secure Artificial Intelligence. First and foremost, we provide consulting services for this purpose. We want you to understand what constitutes the quality of an AI and how to achieve it. In parallel, we are specialists in software quality testing and are your contact when it comes to accompanying development and providing you with detailed advice. Our services include the testing of AI, the formulation as well as the realization of tests to ensure reliable quality assurance - do not hesitate to contact us personally, we will be happy to answer any questions you may have!

In the last years we have learned that our customers also want to build up knowhow in-house. That's why we are proud to offer training as well as workshops in our academy. Among them is the ISTQB Certified Tester AI Testing course, in which we discuss step by step important content on AI and also go into testing methodology for AI. You can also take a course for the "ISTQB® Certified Tester AI Testing" certificate. This course provides insights into AI and explains the associated quality characteristics as well as the development and testing of artificial intelligences. It provides participants with the current state as well as trends in AI testing. Submit a course request now and soon become a certified ISTQB® Certified Tester AI Testing!

Are you looking for a suitable partner to support you in this area? Regardless of whether you want to develop an AI or test with AI, we can help and advise you on your project. We also help you to implement the right quality measures and create trust in your product.

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