Test line coverage for ML is a term used in the fields of artificial intelligence, automation, big data and smart data. It describes how many lines of the programme code of a machine learning system (ML = machine learning) are actually executed during testing.
Imagine you are developing software that can automatically recognise images. During testing, you want to ensure that your programme is actually checked for errors. The test line coverage shows you whether all important parts of the code have been executed in the test process. A high test line coverage means that almost every part of the code has been tested, which reduces the risk of undetected errors.
For example: If your software consists of 1000 lines and your tests only touch 500 of them, your test line coverage is 50 %. The aim is to keep this value as high as possible so that you can be sure that the application runs stably and makes reliable decisions.
Good test line coverage is particularly important in machine learning projects because errors in the code can lead to incorrect results - and these can have far-reaching consequences.
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