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The problem of testing applications working with data encoding lays in data dimension and not in scenario one. Having a good line coverage for encode is not so important than running tests with wide range of data. When encoder works it usually applies rules to data to transform it into one of the forms (encoding or decoding). While rules can be simple they might not fit all input data. Line coverage shows only partial success and sometime it is less than 50% of real world. Most easy option for solving the problem is using biggest possible set of data in tests.
This PR adds two things to client-v2 project:
Test support code to generate datasets for different data types and create metadata (like validation rules) for tests later
Tests that use this new datasets to verify work of implemented readers
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Summary
The problem of testing applications working with data encoding lays in data dimension and not in scenario one. Having a good line coverage for encode is not so important than running tests with wide range of data. When encoder works it usually applies rules to data to transform it into one of the forms (encoding or decoding). While rules can be simple they might not fit all input data. Line coverage shows only partial success and sometime it is less than 50% of real world. Most easy option for solving the problem is using biggest possible set of data in tests.
This PR adds two things to client-v2 project: