To measure and improve the strength of test suites, software projects and their developers commonly use code coverage and aim for a threshold of around 80%. But what is the 80% of the source code that should be covered? To prepare for the development of new, more refined code coverage criteria, we introduce a novel predictive problem in software testing: whether a code line is, or should be, covered by the test suite. In this short paper, we propose the collection of coverage information, source code metrics, and abstract syntax tree data and explore whether they are relevant to predict whether a code line is exercised by the test suite or not. We present a preliminary experiment using four machine learning (ML) algorithms and an open source Java project. We observe that ML classifiers can achieve high accuracy (up to 90%) on this novel predictive problem. We also apply an explainable method to better understand the characteristics of code lines that make them more “appealing” to be covered. Our work opens a research line worth to investigate further, where the focus of the prediction is the code to be tested. Our innovative approach contrasts with most predictive problems in software testing, which aim to predict the test case failure probability.