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Room: C 140 Spl - Gebäude C (Samelson-Campus)
Homepage: https://sse.uni-hildesheim.de/mitglieder/lea-kristin-gerling/ Homepage
Fields of work:
Automated Migration Support for Software Product Line Co-Evolution
Proceedings of the 40th International Conference on Software Engineering (ICSE'18)
The idea of automated migration support arises from the problems observed in practice and the missing solutions for software product line (SPL) co-evolution support. In practice it is common to realize new functionality via unsystematic code cloning: A product is separated from its related SPL and then modified. When a separated product and the SPL evolve over time, this is called SPL co-evolution. During this process, developers have to manually migrate, for example, features or bugfixes between the SPL and the product. Currently, there exists only partial automated solutions for this use case. The presented approach is the first, which aims at using semantic merging to migrate arbitrary semantic units, like features or bugfixes, between a SPL and separated products. The resulting solutions will be evaluated using real and artificial SPLs.
Christian Kröher, Lea Kristin Gerling and Klaus Schmid
Identifying the Intensity of Variability Changes in Software Product Line Evolution
Proceedings of the 22nd International Systems and Software Product Line Conference (SPLC'18)
Best Paper Award
The evolution of a Software Product Line (SPL) typically affects a variety of artifact types. The intensity (the frequency and the amount) in which developers change variability information in these different types of artifacts is currently unknown. In this paper, we present a fine-grained approach for the variability-centric extraction and analysis of changes to code, build, and variability model artifacts introduced by commits. This approach complements existing work that is typically based on a feature-perspective and, thus, abstracts from this level of detail. Further, it provides a detailed understanding of the intensity of changes affecting variability information in these types of artifacts. We apply our approach to the Linux kernel revealing that changes to variability information occur infrequently and only affect small parts of the analyzed artifacts. Further, we outline how these results may improve certain analysis and verification tasks during SPL evolution.