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Auflistung nach Autor:in "Gerling, Lea"

1 - 4 von 4
  • Konferenzbeitrag
    Comparing the intensity of variability changes in software product line evolution (Summary)
    (Software Engineering 2024 (SE 2024), 2024) Kröher, Christian; Gerling, Lea; Schmid, Klaus
  • Konferenzbeitrag
    Control Action Types --– Patterns of Applied Control for Self-adaptive Systems
    (Software Engineering 2024 (SE 2024), 2024) Kröher, Christian; Gerling, Lea; Schmid, Klaus
  • Konferenzbeitrag
    Identifying the Intensity of Variability Changes in Software Product Line Evolution
    (Software Engineering and Software Management 2019, 2019) Kröher, Christian; Gerling, Lea; Schmid, Klaus
    This extended abstract summarizes the paper Identifying the Intensity of Variability Changes in Software Product Line Evolution [KGS18] published in the proceedings of the SPLC 2018 [BBB+18].
  • Konferenzbeitrag
    Incremental Software Product Line Verification - A Performance Analysis with Dead Variable Code
    (Software Engineering 2023, 2023) Kröher, Christian; Flöter, Moritz; Gerling, Lea; Schmid, Klaus
    In this work, we summarize our journal paper published in Empirical Software Engineering (EMSE) in 2022 [Kr22]. Verification approaches for Software Product Lines (SPL) aim at detecting variability-related defects and inconsistencies. In general, these analyses take a significant amount of time to provide complete results for an entire, complex SPL. If the SPL evolves, these results potentially become invalid, which requires a time-consuming re-verification of the entire SPL for each increment. However, in previous work we showed that variability-related changes occur rather infrequently and typically only affect small parts of a SPL. In this paper, we utilize this observation and present an incremental dead variable code analysis as an example for incremental SPL verification, which achieves significant performance improvements. It explicitly considers changes and partially updates its previous results by re-verifying changed artifacts only. We apply this approach to the Linux kernel demonstrating that our fastest incremental strategy takes only 3.20 seconds or less for most of the changes, while the non-incremental approach takes 1,020 seconds in median. We also discuss the impact of different variants of our strategy on the overall performance, providing insights into optimizations that are worthwhile.