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Auflistung nach Schlagwort "Software product lines"

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  • Konferenzbeitrag
    Is There a Mismatch between Real-World Feature Models and Product-Line Research?
    (Software Engineering und Software Management 2018, 2018) Knüppel, Alexander; Thüm, Thomas; Mennicke, Stephan; Meinicke, Jens; Schaefer, Ina
    This work has been presented at the joint meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering in Paderborn, Germany. Feature modeling has emerged as the de-facto standard to capture variability of a software product line in a compact and understandable fashion. Multiple feature modeling languages that evolved over the last decades to manage industrial-size product lines have been proposed. However, less expressive languages, solely permitting require and exclude constraints, are permanently and carelessly used in product-line research. We address the problem whether those less expressive languages are sufficient for industrial product lines. We developed an algorithm to eliminate complex cross-tree constraints in a feature model, enabling the combined usage of tools and algorithms working with different feature model dialects in a plug-and-play manner. However, the scope of our algorithm is limited. Our evaluation on large feature models, including the Linux kernel, gives evidence that require and exclude constraints are not sufficient to express real-world feature models. Hence, we promote that research on feature models needs to consider arbitrary propositional formulas as cross-tree constraints prospectively.
  • 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.