A Feature-Driven Method for Adaptive and Perfective Maintenance
摘要
This manuscript discusses whether the refinement of feature models (FMs) can be applied to fulfill the completion of change requests and satisfy requirement modifications through the Software Maintenance Life Cycle (SMLC). It presents a method that uses FMs as an evolvable artifact to handle software changes as part of adaptive and perfective maintenance. When maintaining a product, we should focus on preserving its features and enhancing them. Our method uses FM composition that guarantees structure-preserving refinements to the underlying FM until the base model is fully updated with the necessary change requests. The empirical research reveals that, on average, around 91% of requested product changes are effectively implemented using our method. It also indicates that the feature-driven method significantly improves the efficiency and success rate of implementing change requests.