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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. In this paper, we compare three transformations (i.e., distributive, Tseitin, and Plaisted-Greenbaum) 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 (i.e., FeatureIDE, KConfigReader, and KClause) 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 certain analyses. Further, the choice of the CNF transformation can substantially influence the performance of subsequent analyses.

Thu 13 Oct

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16:00 - 18:00
Technical Session 32 - Formal Methods and Models IITool Demonstrations / Journal-first Papers / Research Papers at Banquet B
Chair(s): Khouloud Gaaloul University of Michigan - Dearborn
CBMC-SSM: Bounded Model Checking of C Programs with Symbolic Shadow Memory
Tool Demonstrations
Bernd Fischer Stellenbosch University, South Africa, Salvatore La Torre Università degli Studi di Salerno, Gennaro Parlato University of Molise, Peter Schrammel University of Sussex and Diffblue Ltd
Research paper
Tseitin or not Tseitin? The Impact of CNF Transformations on Feature-Model Analyses
Research Papers
Elias Kuiter Otto-von-Guericke-University Magdeburg, Sebastian Krieter University of Ulm, Chico Sundermann University of Ulm, Thomas Thüm University of Ulm, Gunter Saake University of Magdeburg, Germany
A three-valued model abstraction framework for PCTL* stochastic model checkingVirtual
Journal-first Papers
Yang Liu Shanghai Maritime University/National University of Singapore, Yan Ma Nanjing University of Finance and Economics / National University of Singapore, Yongsheng Yang Shanghai Maritime University
File Attached
Research paper
Finding and Understanding Incompleteness Bugs in SMT SolversVirtual
Research Papers
Mauro Bringolf ETH Zurich, Dominik Winterer ETH Zurich, Zhendong Su ETH Zurich
Research paper
Checking LTL Satisfiability via End-to-end LearningVirtual
Research Papers
Weilin Luo School of Computer Science and Engineering, Sun Yat-sen University, Hai Wan School of Data and Computer Science, Sun Yat-sen University, Delong Zhang SUN YAT-SEN UNIVERSITY, Jianfeng Du Guangdong University of Foreign Studies, Hengdi Su SUN YAT-SEN UNIVERSITY
Research paper
QVIP: An ILP-based Formal Verification Approach for Quantized Neural NetworksVirtual
Research Papers
Yedi Zhang ShanghaiTech University, Zhe Zhao ShanghaiTech University, Guangke Chen ShanghaiTech University, Fu Song ShanghaiTech University, Min Zhang East China Normal University, Taolue Chen Birkbeck University of London, Jun Sun Singapore Management University