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AI in Software Testing

Not only is artificial intelligence on the rise, it also holds great potential for quality engineering in software development, especially since it is indispensable for companies.
Viktoria Praschl, Tricentis
October 3, 2023
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This text has been automatically translated from German to English.

As SAP user requirements increase, teams responsible for testing must master ever shorter re-release cycles—often several builds a day for cloud applications. To keep up with this pace, quality assurance can no longer be left for last, but must instead be integrated into all phases of the DevOps process.

What is needed is a shift-left in the V-model: by taking quality into account as early as during the application planning stage, companies can identify and address problems at from an early onset. But how can companies achieve this in the face of shrinking budgets and a labor shortage? Today, test automation is a must. People are also increasingly turning towards artificial intelligence.

CIOs focus on AI


86 percent of CIOs already consider AI an important criterion when selecting quality assurance solutions, according to the Capgemini World Quality Report. According to a survey of DevOps professionals by TechStrong and Tricentis, 90 percent of respondents see significant potential in the use of AI. They expect to see the greatest benefits in the testing area.

UI tests are used to ensure that a user interface works as desired and provides a good user experience. They are usually laborious and cost a good deal of time because they are difficult to automate. AI overcomes this hurdle. By simulating human user behavior, it can perform UI tests autonomously from the user's perspective.

A shift-left approach is particularly difficult in UI testing because testers can usually only start test automation once the user interface has been fully developed, which slows down the release process. AI, on the other hand, can already create test cases from the mock-up design of an application. This allows UI test cases to be designed before the user interface even exists. The same tests can later be applied to the fully developed application.

Visual testing is used to ensure that a user interface works on different end devices. By comparing a baseline screenshot with a future screenshot, problems can be uncovered that are not detected during functional testing at the DOM (Document Object Model) level. An AI can automate visual testing with the prerequisite that it be taught which visual cues to look for.

Even a small change to an application can lead to object identification no longer working and tests breaking down, meaning that quality assurance specialists must continuously check and adapt their automation scripts. This is time-consuming and increases maintenance costs.

Testers spend much of their time troubleshooting. When a test fails, they must painstakingly determine why it failed. AI, on the other hand, can automatically analyze data from failed tests and identify common error patterns. It can then repair common problems—such as incorrect references—automatically.

Testers spend much of their time troubleshooting. When a test fails, they have to painstakingly determine why it didn't work. AI, on the other hand, can automatically analyze data from failed tests and identify common error patterns. It can then repair common problems - such as incorrect references - automatically.

To make testing as efficient as possible, it is important to target the areas that pose the greatest risk. In practice, this transparency is often lacking, which leads many companies to test according to the watering can principle and accumulate a bloated test suite that becomes slower and slower. However, in doing so, they cover only 20 to 40 percent of their business risks.

DevOps and Low-Code with AI


An AI can perform an automated impact analysis to identify the biggest risks. In this manner, it helps quality assurers prioritize the most important tests, as well as to test the correct objectives. AI can also design automated test cases and achieve optimal test coverage. 

To implement a holistic, quality engineering strategy, companies need the best possible technical support. AI helps to integrate test automation into DevOps processes at an early stage. The best way to introduce the new technology is with a low-code/no-code platform, which you can learn more about through our cover story on the subject.

tricentis.com

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Viktoria Praschl, Tricentis

Viktoria Praschl is VP Sales Central Europe at Tricentis


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Working on the SAP basis is crucial for successful S/4 conversion. 

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