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Developing and maintaining open-source software has become an important source of profit for many companies. Change-prone classes in open-source products increase project costs by requiring developers to spend effort and time. Identifying and characterizing change-prone classes can enable developers to focus timely preventive actions, for example, peer-reviews and inspections, on the classes with similar characteristics in the future releases or products. In this study, we collected a set of static metrics and change data at class level from two open-source projects, KOffice and Mozilla. Using these data, we first tested and validated Pareto’s Law which implies that a great majority (around 80%) of change is rooted in a small proportion (around 20%) of classes. Then, we identified and characterized the change-prone classes in the two products by producing tree-based models. In addition, using tree-based …