Test line coverage for ML is a term from the fields of Artificial Intelligence, Automation, and Big Data and Smart Data. It describes how many lines of the program code of a machine learning system (ML = Machine Learning) are actually executed during testing.
Imagine you are developing software that can automatically recognise images. When testing, you want to ensure that your programme is thoroughly checked for errors. Test line coverage shows you whether all important parts of the code have been executed during the testing process. High test line coverage means that almost every part of the code has also been tested, which reduces the risk of undiscovered errors.
For example: If your software consists of 1,000 lines and your tests only cover 500 of them, your line coverage is 50 %. The goal is to keep this value as high as possible, so you can be confident that the application runs stably and makes reliable decisions.
Good test line coverage is particularly important for machine learning projects, as errors in the code can lead to incorrect results – and these can have far-reaching consequences.
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