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Cybercrime causes yearly financial damage close to $600 billion worldwide, nearly one percent of the global GDP (CSIS). This damage can certainly be reduced by producing more secure and reliable software.

State-of-the-art software testing approaches are insufficient. For example, static analysis techniques produce a high rate of false positives. As a result, these approaches require security experts to manually analyze the results and identify real security issues among a large amount of generated warnings. Penetration tests, done entirely by security experts (with some tooling support), are conducted irregularly and are usually separated from the development process.

The core problem is twofold: First, there are too few IT security experts, and second, testing for security and reliability issues are not automated properly. Moreover, manual software testing causes unique challenges in numerous application fields such as:

Internet of Things

When developing for IoT, the code is difficult to execute outside of the embedded device.

This makes it hard to test the code continuously and therefore, ensuring the reliability and security of the product.

Financial Services

In Financial Services, code needs to be secure as well as reliable. Crashes on unusual data inputs are hard to find manually.


Gaming demands constant performance in order to keep the player experience glitch-free. Performance regressions are especially challenging to find and reproduce.

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