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Auflistung nach Autor:in "Sundermann, Chico"

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  • Konferenzbeitrag
    Evaluating State-of-the-Art #SAT Solvers on Industrial Configuration Spaces
    (Software Engineering 2024 (SE 2024), 2024) Sundermann, Chico; Heß, Tobias; Nieke, Michael; Bittner, Paul Maximilian; Young, Jeffrey M.; Thüm, Thomas; Schaefer, Ina
  • Konferenzbeitrag
    It’s Your Loss: Classifying Information Loss During Variability Model Roundtrip Transformations
    (Software Engineering 2023, 2023) Feichtinger, Kevin; Sundermann, Chico; Thüm, Thomas; Rabiser, Rick
    This is a summary of a paper (with the same title) originally published at the 26th ACM International Systems and Software Product Line Conference (SPLC) in 2022 discussing the information loss occurring when transforming variability models.
  • Konferenzbeitrag
    Tseitin or not Tseitin? The Impact of CNF Transformations on Feature-Model Analyses
    (Software Engineering 2023, 2023) Kuiter, Elias; Krieter, Sebastian; Sundermann, Chico; Thüm, Thomas; Saake, Gunter
    This work was published at the 37th IEEE/ACM International Conference on Automated Software Engineering (ASE) 2022 [Ku22]. Feature modeling is widely used to systematically model features of variant-rich software systems and their dependencies. By translating feature models into propositional formulas and analyzing them with solvers, a wide range of automated analyses across all phases of the software development process become possible. Most solvers only accept formulas in conjunctive normal form (CNF), so an additional transformation of feature models is often necessary. However, it is unclear whether this transformation has a noticeable impact on analyses. We compare three transformations for bringing feature-model formulas into CNF. We analyze which transformation can be used to correctly perform feature-model analyses and evaluate three CNF transformation tools on a corpus of 22 real-world feature models. Our empirical evaluation illustrates that some CNF transformations do not scale to complex feature models or even lead to wrong results for model-counting analyses. Further, the choice of the CNF transformation can substantially influence the performance of subsequent analyses.