How Easy is SAT-Based Analysis of a Feature Model?
Authors: 
Elias Kuiter, 
Tobias Heß, 
Chico Sundermann, 
Sebastian Krieter, 
Thomas Thüm, Gunter SaakeAuthors Info & Claims
Elias Kuiter, Tobias Heß, Chico Sundermann, Sebastian Krieter, Thomas Thüm, Gunter SaakeAuthors Info & ClaimsPages 149 - 151
Abstract
With feature-model analyses, stakeholders can improve their understanding of complex configuration spaces. Computationally, these analyses are typically reduced to solving satisfiability problems. While this has been found to perform reasonably well on many models, estimating the efficiency of a given analysis on a given model is still difficult. We argue that such estimates are necessary due to the heterogeneity of feature models. We discuss inherently influential factors and suggest potential algorithmic solutions.
References
[1]
M. Acher, P. Collet, P. Lahire, and R. B. France. 2011. Slicing Feature Models. In ASE. IEEE, 424–427.
[2]
M. Acher, P. Heymans, P. Collet, C. Quinton, P. Lahire, and P. Merle. 2012. Feature Model Differences. In CAiSE. Springer, 629–645.
[3]
R. Adamy, E. Kuiter, and G. Saake. 2023. Exploiting Structure: A Survey and Analysis of Structures and Hardness Measures for Propositional Formulas. Technical Report. University of Magdeburg. https://doi.org/10.32388/7U1PFG Qeios:7U1PFG
[4]
T. N. Alyahya, M. E. B. Menai, and H. Mathkour. 2022. On the Structure of the Boolean Satisfiability Problem: A Survey. ACM Comput. Surv. 55, 3, Article 46 (2022). https://doi.org/10.1145/3491210
[5]
C. Ansótegui, M. L. Bonet, and J. Levy. 2009. On the Structure of Industrial SAT Instances. In CP. Springer, 127–141.
[6]
S. Apel, D. Batory, C. Kästner, and G. Saake. 2013. Feature-Oriented Software Product Lines. Springer.
[7]
D. Batory. 2005. Feature Models, Grammars, and Propositional Formulas. In SPLC. Springer, 7–20.
[8]
D. Benavides, S. Segura, and A. Ruiz-Cortés. 2010. Automated Analysis of Feature Models 20 Years Later: A Literature Review. Information Systems 35, 6 (2010), 615–708.
[9]
T. Berger and P. Collet. 2019. Usage Scenarios for a Common Feature Modeling Language. In SPLC. ACM, 174–181.
[10]
T. Berger, R. Rublack, D. Nair, J. M. Atlee, M. Becker, K. Czarnecki, and A. Wąsowski. 2013. A Survey of Variability Modeling in Industrial Practice. In VaMoS. ACM, 7:1–7:8.
[11]
T. Berger, S. She, R. Lotufo, A. Wąsowski, and K. Czarnecki. 2013. A Study of Variability Models and Languages in the Systems Software Domain. TSE 39, 12 (2013), 1611–1640.
[12]
A. Biere, N. Froleyks, and W. Wang. 2023. CadiBack: Extracting Backbones with CaDiCaL.
[13]
T. Castro, L. Teixeira, V. Alves, S. Apel, M. Cordy, and R. Gheyi. 2021. A Formal Framework of Software Product Line Analyses. TOSEM 30, 3, Article 34 (2021).
[14]
S. Chaudhuri. 1998. An overview of query optimization in relational systems. In Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems. 34–43.
[15]
P. C. Clements, J. D. McGregor, and S. G. Cohen. 2005. The Structured Intuitive Model for Product Line Economics (SIMPLE). Technical Report. Carnegie-Mellon University.
[16]
S. El-Sharkawy, A. Krafczyk, and K. Schmid. 2015. Analysing the KConfig Semantics and its Analysis Tools. In GPCE. ACM, 45–54.
[17]
P. Franz, T. Berger, I. Fayaz, S. Nadi, and E. Groshev. 2021. ConfigFix: Interactive Configuration Conflict Resolution for the Linux Kernel. In ICSE-SEIP. IEEE, 91–100.
[18]
J. A. Galindo, M. Acher, J. M. Tirado, C. Vidal, B. Baudry, and D. Benavides. 2016. Exploiting the Enumeration of All Feature Model Configurations: A New Perspective With Distributed Computing. In SPLC. ACM, 74–78.
[19]
T. Heß, C. Sundermann, and T. Thüm. 2021. On the Scalability of Building Binary Decision Diagrams for Current Feature Models. In SPLC. ACM, 131–135.
[20]
M. Janota, I. Lynce, and J. Marques-Silva. 2015. Algorithms for computing backbones of propositional formulae. Ai Communications 28, 2 (2015), 161–177.
[21]
K. C. Kang, S. G. Cohen, J. A. Hess, W. E. Novak, and A. S. Peterson. 1990. Feature-Oriented Domain Analysis (FODA) Feasibility Study. Technical Report CMU/SEI-90-TR-21. Software Engineering Institute.
[22]
C. Kästner, P. G. Giarrusso, T. Rendel, S. Erdweg, K. Ostermann, and T. Berger. 2011. Variability-Aware Parsing in the Presence of Lexical Macros and Conditional Compilation. In OOPSLA. ACM, 805–824.
[23]
A. Kenner, C. Kästner, S. Haase, and T. Leich. 2010. TypeChef: Toward Type Checking #Ifdef Variability in C. In FOSD. ACM, 25–32.
[24]
A. Knüppel, T. Thüm, S. Mennicke, J. Meinicke, and I. Schaefer. 2017. Is There a Mismatch Between Real-World Feature Models and Product-Line Research?. In ESEC/FSE. ACM, 291–302.
[25]
S. Krieter, R. Arens, M. Nieke, C. Sundermann, T. Heß, T. Thüm, and C. Seidl. 2021. Incremental Construction of Modal Implication Graphs for Evolving Feature Models. In SPLC. ACM, 64–74.
[26]
S. Krieter, R. Schröter, T. Thüm, W. Fenske, and G. Saake. 2016. Comparing Algorithms for Efficient Feature-Model Slicing. In SPLC. ACM, 60–64.
[27]
E. Kuiter, A. Knüppel, T. Bordis, T. Runge, and I. Schaefer. 2022. Verification Strategies for Feature-Oriented Software Product Lines. In VaMoS. ACM, 12:1–12:9.
[28]
E. Kuiter, S. Krieter, C. Sundermann, T. Thüm, and G. Saake. 2022. Tseitin or not Tseitin? The Impact of CNF Transformations on Feature-Model Analyses. In ASE. ACM, 110:1–110:13.
[29]
J. Lee, S. Kang, and D. Lee. 2012. A Survey on Software Product Line Testing. In SPLC. ACM, 31–40.
[30]
L. Lesoil, M. Acher, A. Blouin, and J.-M. Jézéquel. 2021. Deep Software Variability: Towards Handling Cross-Layer Configuration. In VaMoS. ACM, 10:1–10:8.
[31]
J. H. Liang, V. Ganesh, K. Czarnecki, and V. Raman. 2015. SAT-Based Analysis of Large Real-World Feature Models Is Easy. In SPLC. Springer, 91–100.
[32]
J. Liebig, A. von Rhein, C. Kästner, S. Apel, J. Dörre, and C. Lengauer. 2013. Scalable Analysis of Variable Software. In ESEC/FSE. ACM, 81–91.
[33]
R. E. Lopez-Herrejon and D. Batory. 2001. A Standard Problem for Evaluating Product-Line Methodologies. In GCSE. Springer, 10–24.
[34]
G. Masina, G. Spallitta, and R. Sebastiani. 2023. On CNF Conversion for SAT Enumeration. arxiv:2303.14971 [cs.LO]
[35]
F. Medeiros, C. Kästner, M. Ribeiro, R. Gheyi, and S. Apel. 2016. A Comparison of 10 Sampling Algorithms for Configurable Systems. In ICSE. ACM, 643–654.
[36]
M. Mendonça, A. Wąsowski, and K. Czarnecki. 2009. SAT-Based Analysis of Feature Models is Easy. In SPLC. Software Engineering Institute, 231–240.
[37]
M. Mendonça, A. Wąsowski, K. Czarnecki, and D. Cowan. 2008. Efficient Compilation Techniques for Large Scale Feature Models. In GPCE. ACM, 13–22.
[38]
M. W. Moskewicz, C. F. Madigan, Y. Zhao, L. Zhang, and S. Malik. 2001. Chaff: Engineering an Efficient SAT Solver. In DAC. ACM, 530–535.
[39]
D.-J. Munoz, J. Oh, M. Pinto, L. Fuentes, and D. Batory. 2019. Uniform Random Sampling Product Configurations of Feature Models That Have Numerical Features. In SPLC. ACM, 289–301.
[40]
D.-J. Munoz, M. Pinto, L. Fuentes, and D. Batory. 2023. Transforming Numerical Feature Models into Propositional Formulas and the Universal Variability Language. Journal of Systems and Software 204 (2023), 111770. https://doi.org/10.1016/j.jss.2023.111770
[41]
Z. Newsham, W. Lindsay, V. Ganesh, J. H. Liang, S. Fischmeister, and K. Czarnecki. 2015. SATGraf: Visualizing the Evolution of SAT Formula Structure in Solvers. In Theory and Applications of Satisfiability Testing – SAT 2015, Marijn Heule and Sean Weaver (Eds.). Springer International Publishing, Cham, 62–70.
[42]
J. Oh, N. F. Yıldıran, J. Braha, and P. Gazzillo. 2021. Finding Broken Linux Configuration Specifications by Statically Analyzing the Kconfig Language. In ESEC/FSE. ACM, 893–905.
[43]
L. Passos, M. Novakovic, Y. Xiong, T. Berger, K. Czarnecki, and A. Wąsowski. 2011. A Study of Non-Boolean Constraints in Variability Models of an Embedded Operating System. In Proceedings of the 15th International Software Product Line Conference, Volume 2 (Munich, Germany) (SPLC ’11). ACM, New York, NY, USA, Article 2. https://doi.org/10.1145/2019136.2019139
[44]
Q. Plazar, M. Acher, G. Perrouin, X. Devroey, and M. Cordy. 2019. Uniform Sampling of SAT Solutions for Configurable Systems: Are We There Yet?. In ICST. IEEE, 240–251.
[45]
K. Pohl, G. Böckle, and F. J. van der Linden. 2005. Software Product Line Engineering: Foundations, Principles and Techniques. Springer.
[46]
R. Pohl, K. Lauenroth, and K. Pohl. 2011. A Performance Comparison of Contemporary Algorithmic Approaches for Automated Analysis Operations on Feature Models. In ASE. IEEE, 313–322.
[47]
P.-Y. Schobbens, P. Heymans, and J.-C. Trigaux. 2006. Feature Diagrams: A Survey and a Formal Semantics. In RE. IEEE, 136–145.
[48]
R. Schröter, S. Krieter, T. Thüm, F. Benduhn, and G. Saake. 2016. Feature-Model Interfaces: The Highway to Compositional Analyses of Highly-Configurable Systems. In ICSE. ACM, 667–678.
[49]
J. Siegmund, N. Siegmund, and S. Apel. 2015. Views on Internal and External Validity in Empirical Software Engineering. In ICSE. IEEE, 9–19.
[50]
C. Sundermann, T. Heß, M. Nieke, P. M. Bittner, J. M. Young, T. Thüm, and I. Schaefer. 2023. Evaluating State-of-the-Art #SAT Solvers on Industrial Configuration Spaces. EMSE 28 (2023).
[51]
C. Sundermann, E. Kuiter, T. Heß, H. Raab, S. Krieter, and T. Thüm. 2023. On the Benefits of Knowledge Compilation for Feature-Model Analyses. (2023). Accepted.
[52]
C. Sundermann, M. Nieke, P. M. Bittner, T. Heß, T. Thüm, and I. Schaefer. 2021. Applications of #SAT Solvers on Feature Models. In VaMoS. ACM, Article 12.
[53]
C. Sundermann, H. Raab, T. Heß, T. Thüm, and I. Schaefer. 2023. Exploiting d-DNNFs for Repetitive Counting Queries on Feature Models. Technical Report arXiv:2303.12383. Cornell University Library.
[54]
C. Sundermann, S. Vill, T. Thüm, K. Feichtinger, P. Agarwal, R. Rabiser, J. A. Galindo, and D. Benavides. 2023. UVLParser: Extending UVL with Language Levels and Conversion Strategies. In SPLC. ACM, 39–42.
[55]
T. Thüm. 2020. A BDD for Linux? The Knowledge Compilation Challenge for Variability. In SPLC. ACM, Article 16.
[56]
T. Thüm, S. Apel, C. Kästner, I. Schaefer, and G. Saake. 2014. A Classification and Survey of Analysis Strategies for Software Product Lines. CSUR 47, 1 (2014), 6:1–6:45.
[57]
T. Thüm, D. Batory, and C. Kästner. 2009. Reasoning About Edits to Feature Models. In ICSE. IEEE, 254–264.
[58]
L. Xu, F. Hutter, H. H. Hoos, and K. Leyton-Brown. 2008. SATzilla: Portfolio-Based Algorithm Selection for SAT. JAIR 32 (2008), 565–606.
[59]
J. M. Young, P. M. Bittner, E. Walkingshaw, and T. Thüm. 2022. Variational Satisfiability Solving: Efficiently Solving Lots of Related SAT Problems. EMSE 28 (2022).
[60]
W. Zhang, H. Zhao, and H. Mei. 2004. A Propositional Logic-Based Method for Verification of Feature Models. In ICFEM. Springer, 115–130.
Index Terms
- How Easy is SAT-Based Analysis of a Feature Model?
Recommendations
SAT-based analysis of feature models is easy
SPLC '09: Proceedings of the 13th International Software Product Line ConferenceFeature models are a popular variability modeling notation used in product line engineering. Automated analyses of feature models, such as consistency checking and interactive or offline product selection, often rely on translating models to ...
Tseitin or not Tseitin? The Impact of CNF Transformations on Feature-Model Analyses
ASE '22: Proceedings of the 37th IEEE/ACM International Conference on Automated Software EngineeringFeature 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 ...
SAT-based analysis of large real-world feature models is easy
SPLC '15: Proceedings of the 19th International Conference on Software Product LineModern conflict-driven clause-learning (CDCL) Boolean SAT solvers provide efficient automatic analysis of real-world feature models (FM) of systems ranging from cars to operating systems. It is well-known that solver-based analysis of real-world FMs ...
Comments
Information & Contributors
Information
Published In
February 2024
172 pages
ISBN:9798400708770
DOI:10.1145/3634713
Copyright © 2024 Owner/Author.
This work is licensed under a Creative Commons Attribution International 4.0 License.
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 07 February 2024
Check for updates
Author Tags
Qualifiers
- Short-paper
- Research
- Refereed limited
Conference
VaMoS 2024
VaMoS 2024: 18th International Working Conference on Variability Modelling of Software-Intensive Systems
February 7 - 9, 2024
Bern, Switzerland
Acceptance Rates
Overall Acceptance Rate 66 of 147 submissions, 45%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 92Total Downloads
- Downloads (Last 12 months)92
- Downloads (Last 6 weeks)13
Reflects downloads up to 10 Aug 2024
Other Metrics
Citations
View Options
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML FormatGet Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in