{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/sat-based-analysis-of-large-real-world","title":"SAT-based Analysis of Large Real-world Feature Models is Easy","arxiv_id":"1506.05198","date":"2015-06-17","proceeding":null,"authors":["Jia Hui Liang","Vijay Ganesh","Venkatesh Raman","Krzysztof Czarnecki"],"abstract":"Modern conflict-driven clause-learning (CDCL) Boolean SAT solvers provide\nefficient automatic analysis of real-world feature models (FM) of systems\nranging from cars to operating systems. It is well-known that solver-based\nanalysis of real-world FMs scale very well even though SAT instances obtained\nfrom such FMs are large, and the corresponding analysis problems are known to\nbe NP-complete. To better understand why SAT solvers are so effective, we\nsystematically studied many syntactic and semantic characteristics of a\nrepresentative set of large real-world FMs. We discovered that a key reason why\nlarge real-world FMs are easy-to-analyze is that the vast majority of the\nvariables in these models are unrestricted, i.e., the models are satisfiable\nfor both true and false assignments to such variables under the current partial\nassignment. Given this discovery and our understanding of CDCL SAT solvers, we\nshow that solvers can easily find satisfying assignments for such models\nwithout too many backtracks relative to the model size, explaining why solvers\nscale so well. Further analysis showed that the presence of unrestricted\nvariables in these real-world models can be attributed to their high-degree of\nvariability. Additionally, we experimented with a series of well-known\nnon-backtracking simplifications that are particularly effective in solving\nFMs. The remaining variables/clauses after simplifications, called the core,\nare so few that they are easily solved even with backtracking, further\nstrengthening our conclusions.","url_abs":"http://arxiv.org/abs/1506.05198v3","url_pdf":"http://arxiv.org/pdf/1506.05198v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"sat-based-analysis-of-large-real-world","repo_url":"https://github.com/JLiangWaterloo/fmeasy","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}