@article{30511,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>Many critical codebases are written in C, and most of them use preprocessor directives to encode variability, effectively encoding software product lines. These preprocessor directives, however, challenge any static code analysis. SPLlift, a previously presented approach for analyzing software product lines, is limited to Java programs that use a rather simple feature encoding and to analysis problems with a finite and ideally small domain. Other approaches that allow the analysis of real-world C software product lines use special-purpose analyses, preventing the reuse of existing analysis infrastructures and ignoring the progress made by the static analysis community. This work presents <jats:sc>VarAlyzer</jats:sc>, a novel static analysis approach for software product lines. <jats:sc>VarAlyzer</jats:sc> first transforms preprocessor constructs to plain C while preserving their variability and semantics. It then solves any given distributive analysis problem on transformed product lines in a variability-aware manner. <jats:sc>VarAlyzer</jats:sc> ’s analysis results are annotated with feature constraints that encode in which configurations each result holds. Our experiments with 95 compilation units of OpenSSL show that applying <jats:sc>VarAlyzer</jats:sc> enables one to conduct inter-procedural, flow-, field- and context-sensitive data-flow analyses on entire product lines for the first time, outperforming the product-based approach for highly-configurable systems.</jats:p>}},
  author       = {{Schubert, Philipp and Gazzillo, Paul and Patterson, Zach and Braha, Julian and Schiebel, Fabian Benedikt and Hermann, Ben and Wei, Shiyi and Bodden, Eric}},
  issn         = {{0928-8910}},
  journal      = {{Automated Software Engineering}},
  keywords     = {{inter-procedural static analysis, software product lines, preprocessor, LLVM, C/C++}},
  number       = {{1}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Static data-flow analysis for software product lines in C}}},
  doi          = {{10.1007/s10515-022-00333-1}},
  volume       = {{29}},
  year         = {{2022}},
}

@inproceedings{18249,
  abstract     = {{Augmented Reality (AR) has recently found high attention in mobile shopping apps such as in domains like furniture or decoration. Here, the developers of the apps focus on the positioning of atomic 3D objects in the physical environment. With this focus, they neglect the configuration of multi-faceted 3D object composition according to the user needs and environmental constraints. To tackle these challenges, we present a model-based approach to support AR-assisted product con-figuration based on the concept of Dynamic Software Product Lines. Our approach splits products (e.g. table) into parts (e.g. tabletop, ta-ble legs, funnier) with their 3D objects and additional information (e.g. name, price). The possible products, which can be configured out of these parts, are stored in a feature model. At runtime, this feature model can be used to configure 3D object compositions out of the product parts and adapt to user needs and environmental constraints. The benefits of this approach are demonstrated by a case study of configuring modular kitchens with the help of a prototypical mobile-based implementation.}},
  author       = {{Gottschalk, Sebastian and Yigitbas, Enes and Schmidt, Eugen and Engels, Gregor}},
  booktitle    = {{Human-Centered Software Engineering. HCSE 2020}},
  editor       = {{Bernhaupt, Regina and Ardito, Carmelo and Sauer, Stefan}},
  keywords     = {{Product Configuration, Augmented Reality, Runtime Adaptation, Dynamic Software Product Lines}},
  location     = {{Eindhoven}},
  publisher    = {{Springer}},
  title        = {{{Model-based Product Configuration in Augmented Reality Applications}}},
  doi          = {{10.1007/978-3-030-64266-2_5}},
  volume       = {{12481}},
  year         = {{2020}},
}

@inproceedings{44,
  abstract     = {{Natural language software requirements descriptions enable end users to formulate their wishes and expectations for a future software product without much prior knowledge in requirements engineering. However, these descriptions are susceptible to linguistic inaccuracies such as ambiguities and incompleteness that can harm the development process. There is a number of software solutions that can detect deficits in requirements descriptions and partially solve them, but they are often hard to use and not suitable for end users. For this reason, we develop a software system that helps end-users to create unambiguous and complete requirements descriptions by combining existing expert tools and controlling them using automatic compensation strategies. In order to recognize the necessity of individual compensation methods in the descriptions, we have developed linguistic indicators, which we present in this paper. Based on these indicators, the whole text analysis pipeline is ad-hoc configured and thus adapted to the individual circumstances of a requirements description.}},
  author       = {{Bäumer, Frederik Simon and Geierhos, Michaela}},
  booktitle    = {{Proceedings of the 51st Hawaii International Conference on System Sciences}},
  isbn         = {{978-0-9981331-1-9}},
  keywords     = {{Software Product Lines: Engineering, Services, and Management, Ambiguities, Incompleteness, Natural Language Processing, Software Requirements}},
  location     = {{Big Island, Waikoloa Village}},
  pages        = {{5746--5755}},
  title        = {{{Flexible Ambiguity Resolution and Incompleteness Detection in Requirements Descriptions via an Indicator-based Configuration of Text Analysis Pipelines}}},
  doi          = {{10125/50609}},
  year         = {{2018}},
}