Towards Effective and Efficient Feature-Model Analyses for Evolving System Software

Software-intensive systems are often configurable, with system software frequently being developed as product lines to manage variability.Feature models describe the configurable features and their dependencies in such product lines, enabling automated analysis via reasoning tools.However, some challenges remain: Feature models must be correctly extracted and transformed for effective analysis, their complexity can hamper efficiency, and their evolution over time may exacerbate these issues.In the proposed thesis, we address these challenges by (1) evaluating the impact of extraction and transformation on analysis effectiveness and efficiency, (2) studying long-term evolution trends in the configurability and computational complexity of feature models, and (3) studying individual evolution steps, so practitioners can better assess the impact of updates on end users.Our overall goal is to improve our understanding of large, real-world feature models and their evolution.