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* Add a check to ensure that if the formulas originating from the exhaustivity /Gerard Basler2015-03-021-1/+1
| | | | | | | | | | | | | | | | | | reachability analysis are too big to be solved in reasonable time, then we skip the analysis. I also cleaned up warnings. Why did `t9181.scala` work fine with 2.11.4, but is now running out of memory? In order to ensure that the scrutinee is associated only with one of the 400 derived classes we add 400*400 / 2 ~ 80k clauses to ensure mutual exclusivity. In 2.11.4 the translation into CNF used to fail, since it would blow up already at this point in memory. This has been fixed in 2.11.5, but now the DPLL solver is the bottleneck. There's a check in the search for all models (exhaustivity) that it would avoid a blow up, but in the search for a model (reachability), such a check is missing.
* SI-6942 more efficient unreachability analysisAdriaan Moors2013-01-171-0/+1
Avoid blowing the stack/the analysis budget by more eagerly translating the propositions that model matches to CNF. First building a large proposition that represents the match, and then converting to CNF tends to blow the stack. Luckily, it's easy to convert to CNF as we go. The optimization relies on `CNF(P1 /\ ... /\ PN) == CNF(P1) ++ CNF(...) ++ CNF(PN)`: Normalizing a conjunction of propositions yields the same formula as concatenating the normalized conjuncts. CNF conversion is expensive for large propositions, so we push it down into the conjunction and then concatenate the resulting arrays of clauses (which is cheap). (CNF converts a free-form proposition into an `Array[Set[Lit]]`, where: - the Array's elements are /\'ed together; - and the Set's elements are \/'ed; - a Lit is a possibly negated variable.) NOTE: - removeVarEq may throw an AnalysisBudget.Exception - also reworked the interface used to build formula, so that we can more easily plug in SAT4J when the time comes