Feature annotation based on preprocessor directives is the most common mechanism in Highly-Configurable Software Systems (HCSSs) to select the features to be included in a system variant. However, it is challenging to understand, maintain, and evolve feature fragments guarded by #ifdef directives. Yet, despite HCSSs being implemented in Version Control Systems, the support for evolving features in space and time is still challenging. To extend the knowledge on this topic, we analyze the feature life cycle in space and time. Specifically, we introduce an automated mining approach and apply it to four HCSSs, analyzing commits of their entire development life cycle (13 to 20 years and 37500 commits). This goes beyond existing studies, which investigated only differences between releases or entire systems. Our results show that features undergo frequent changes, often with substantial modifications of their code. The findings of our empirical analyses stress the need for better support of system evolution in space and time at the level of features. In addition to these analyses, we contribute with an automated mining approach for the analysis of system evolution at the level of features. Furthermore, we also make available our dataset for new studies on feature evolution in HCSSs.