{"id":166996,"date":"2014-07-01T00:00:00","date_gmt":"2014-07-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/approximations-for-model-construction-2\/"},"modified":"2018-10-16T21:26:07","modified_gmt":"2018-10-17T04:26:07","slug":"approximations-for-model-construction-2","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/approximations-for-model-construction-2\/","title":{"rendered":"Approximations for Model Construction"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We consider the problem of efficiently computing models for satisfiable constraints, in the presence of complex background theories such as oating-point arithmetic. Model construction has various applications, for instance the automatic generation of test inputs. It is well-known that naive encoding of constraints into simpler theories (for instance, bit-vectors or propositional logic) can lead to a drastic increase in size, and be unsatisfactory in terms of memory and runtime needed for model construction. We define a framework for systematic application of approximations in order to speed up model construction. Our method is more general than previous techniques in the sense that approximations that are neither under- nor over-approximations can be used, and shows promising results in practice.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We consider the problem of efficiently computing models for satisfiable constraints, in the presence of complex background theories such as oating-point arithmetic. Model construction has various applications, for instance the automatic generation of test inputs. It is well-known that naive encoding of constraints into simpler theories (for instance, bit-vectors or propositional logic) can lead to [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"Springer","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Proceedings of the 7th International Joint Conference on Automated Reasoning (IJCAR 2014)","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"LNAI","msr_volume":"8562","msr_copyright":"","msr_conference_name":"Proceedings of the 7th International Joint Conference on Automated Reasoning (IJCAR 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