Detection of Seed Methods for Quantification of Feature Confinement

The way features are implemented in source code has a significant influence on multiple quality aspects of a software system. Hence, it is important to regularly evaluate the quality of feature confinement. Unfortunately, existing approaches to such measurement rely on expert judgement for tracing links between features and source code which hinders the ability to perform cost-efficient and consistent evaluations over time or on a large portfolio of systems.
In this paper, we propose an approach to automating measurement of feature confinement by detecting the methods which play a central role in implementations of features, the so-called seed methods, and using them as starting points for a static slicing algorithm. We show that this approach achieves the same level of performance compared to the use of manually identified seed methods. Furthermore we illustrate the scalability of the approach by tracking the evolution of feature scattering and tangling in an open-source project over a period of ten years.