d42 in work: best practices of data generation

Test data generation is a critical part of automated testing. However, many companies use different approaches, from static data in JSON format to random generation, without control over generated values in tests where it matters. Or, on the contrary, excessive control of a part of the data that is not important for the scenario being checked. These approaches lead to unstable tests and increased maintenance complexity. 

In the presentation, I will tell how to properly prepare data for tests, where control over values in fields is important, and where it is an extra context, how to maintain data consistency with the API, how to generate and store data. 

 In the presentation, I will use the d42 library as examples (https://github.com/tsv1/d42).