Variational satisfiability solving: efficiently solving lots of related SAT problems

Abstract Incremental satisfiability (SAT) solving is an extension of classic SAT that enables a set related problems by identifying and exploiting shared terms. However, using incremental solvers effectively hard since performance sensitive to the input order subterms results must be tracked manually. For analyses generate sets problems, such as those in software product lines, are either not used or their use clearly described literature. This paper translates ordering problem encoding automates solving. We introduce variational solving, which differs from accepting all single returning output. Variational syntactically encodes differences local points variation. With this syntax, our approach interaction with solver method automatically optimize sharing input. To evaluate these ideas, we formalize algorithm, construct prototype solver, perform empirical analysis on two real-world datasets applied evolution scenarios. show, assuming input, scales better for than four off-the-shelf while also tracking individual results.