{"id":3744,"date":"2026-09-22T17:56:56","date_gmt":"2026-09-22T15:56:56","guid":{"rendered":"https:\/\/itpower.de\/en\/?page_id=3744"},"modified":"2026-09-23T13:57:40","modified_gmt":"2026-09-23T11:57:40","slug":"testing-ai-why-traditional-software-testing-isnt-enough","status":"publish","type":"page","link":"https:\/\/itpower.de\/en\/blog\/testing-ai-why-traditional-software-testing-isnt-enough\/","title":{"rendered":"Testing AI: Why Traditional Software Testing Isn’t Enough"},"content":{"rendered":"\t\t
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Testing AI: Why The Traditional Approach Isn’t Sufficient<\/strong><\/h1>

Traditional software<\/b> operates according to hard-coded rules. If the result of a function deviates from the expected behavior, the error can often be pinpointed in the program code and specifically corrected. But how does quality assurance work when a system\u2019s behavior isn\u2019t determined solely by predefined rules, but is learned from training data?<\/span><\/p>

This is where artificial intelligence (AI)<\/b> presents new challenges for software quality assurance. An AI-based image recognition system, for example, can correctly identify thousands of objects yet still misclassify individual images. Such a misclassification often cannot be traced back to a specific line of code and fixed in a targeted manner.<\/p>

For testing, this means a fundamental shift in perspective: Instead of focusing solely on detecting individual errors, the quality of an AI system must be statistically<\/b> evaluated across a large number of test cases. What matters is not only the number of tests but also how well the test data used reflects the future operational environment.<\/p>

But under these conditions, how can we reliably determine whether an AI system meets the required quality standards?<\/p><\/div>

Traditional software and AI follow different principles<\/strong><\/span><\/h2>

AI systems based on machine learning<\/b> differ fundamentally from traditionally programmed systems. They learn from a large number of training examples by adjusting a model\u2019s parameters during training. For example, an image recognition system learns to distinguish between dogs and cats based on numerous images. The underlying features are not entirely dictated by fixed rules but are derived from the training data.<\/p>

This has direct implications for quality assurance: Even a more powerful AI model will not necessarily process every input correctly. It is therefore crucial to determine how frequently <\/b>errors occur and under what conditions<\/b> they arise.<\/p>

The quality of an AI system must therefore also be evaluated statistically. Rather than focusing exclusively on individual test results, the question becomes how reliably the system performs across a wide variety of different inputs.<\/p><\/div>

1.000 test cases \u2013 and still different results?<\/strong><\/h2>

A simple example illustrates how much the selection of test data influences the result: An AI-based image recognition system distinguishes between dogs and cats. It recognizes cats with 95 percent accuracy, but dogs with only 85 percent.<\/p>

Two testers are tasked with independently evaluating the system\u2019s quality. Both use 1,000 images each, but select different test datasets. While the first tester uses mostly images of cats, the second uses primarily images of dogs.<\/p>

The results differ significantly:<\/p><\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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Figure 1: Test Data and Accuracy<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t
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Although both testers evaluate the same system using the same number of images, they obtain different results. However, neither of the two test datasets reflects the actual distribution of dogs and cats assumed in the example. When this distribution is taken into account, the accuracy is approximately 90.7 percent.<\/p>

This example illustrates a fundamental problem with AI testing: a large number of test cases alone does not guarantee meaningful results. It is also crucial that the test data realistically reflects the conditions<\/b> of the system\u2019s future application.<\/p>

Otherwise, even extensive testing can provide a distorted picture of an AI system\u2019s actual performance. Only a representative selection<\/b> of test data enables reliable statistical assessments of quality.<\/p><\/div>

Why Test Coverage Alone Is Not Enough for AI<\/strong><\/h2>

In traditional software development, systematic testing procedures ensure that as many relevant functions and input scenarios as possible are verified. Methods such as equivalence class partitioning or combinatorial testing help cover extensive test scenarios with a sufficient number of tests.<\/p>

Test coverage is also important when testing AI systems. However, it is not sufficient on its own.<\/p>

To obtain reliable test results, two requirements must therefore be met:<\/p><\/div>