![]() In this case, synthetic test data is a possible solution, but that also requires some effort to get reliable results from.įinally, compliance with regulations may be the most important consideration when you’re generating test data. ![]() Other ML solutions address problems like image or video processing in which data for training and testing might be expensive. For example, neural networks are one of the most common ML techniques for forecasting, but it requires training a large amount of data. Testing data also needs to be aware of the specific requirements of machine learning (ML) based solutions. Simple unit tests usually require that not only best-case scenarios are tested, but minimum and maximum possible values for arguments as well, to detect possible overflows or inefficient algorithms. A good test plan must address not only business requirements but also edge cases, the cost of generating data, and privacy and regulatory compliance. Business requirements are often plagued with ambiguity or are defined too narrowly. Why Is Generating Reliable Test Data So Hard?Īt first glance, generating test data should be an easy checkmark in your test plan, but it’s usually quite the opposite. ![]() In this article, you’ll learn what criteria your data should meet in order to be considered good for testing, how to improve the quality of your test data, and which tools are helpful for generating general well-known domain data and test data for special use cases. Fortunately, there are a few best practices that can help you create that dataset reliably. ![]() It’s a fact that good testing requires good test data. ![]()
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