Features are the key abstraction to develop and maintain software product lines. A challenge faced in the maintenance of product lines is the understanding of the dependencies that exist between features. In the source code, a feature dependency occurs whenever program elements within the boundaries of a feature s implementation depend on elements external to that feature. Examples are either attributes or methods defined in the realisation of a feature, but used in the code realising other features. As developers modify the source code associated with a feature, they must ensure that other features are consistently updated with the new changes – the so-called change propagation. However, appropriate change propagation is far from being trivial as features are often not modularised in the source code. In this way, given a change in a certain feature, it is challenging to reveal which (part of) other features should also change. Change propagation becomes, therefore, a central and non-trivial aspect of software product-line maintenance. Developers may overlook important parts of the code that should be revised or changed, thus not fully propagating changes. Conversely, they may also unnecessarily analyse parts that are not relevant to the feature-maintenance task at hand, thereby increasing the maintenance effort or even mis-propagating changes. The creation of a good mental model based on the structure of feature dependencies becomes essential for gaining insight into the intricate relationship between features in order to properly propagate changes. Unfortunately, there is no understanding in the state of the art about structural properties of feature dependencies that affect change propagation. This understanding is not yet possible as: (i) there is no conceptual characterisation and quantification means for structural properties of feature dependency, and (ii) there is no empirical investigation on the influence of these properties on change propagation. In this context, this thesis presents three contributions to overcome the aforementioned problems. First, we develop a study to understand change propagation in presence of feature dependencies in several industry-strength product lines. Second, we propose a measurement framework intended to quantify structural properties of feature dependencies. We also develop a study revealing that conventional metrics typically used in previous research, such as coupling metrics, are not effective indicators of change propagation in software product lines. Our proposed metrics consistently outperformed conventional metrics. Third, we also propose a method to support change propagation by facing the organisation of feature dependency information as a clustering problem. We evaluate if our proposed organisation has potential to help developers to propagate changes in software product lines.