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Sharpening constraint programming approaches for bit-vector theory. (English) Zbl 06756571
Salvagnin, Domenico (ed.) et al., Integration of AI and OR techniques in constraint programming. 14th international conference, CPAIOR 2017, Padua, Italy, June 5–8, 2017. Proceedings. Cham: Springer (ISBN 978-3-319-59775-1/pbk; 978-3-319-59776-8/ebook). Lecture Notes in Computer Science 10335, 3-20 (2017).
Summary: We address the challenge of developing efficient constraint programming-based approaches for solving formulas over the quantifier-free fragment of the theory of bitvectors (BV), which is of paramount importance in software verification. We propose CP(BV), a highly efficient BV resolution technique built on carefully chosen anterior results sharpened with key original features such as thorough domain combination or dedicated labeling. Extensive experimental evaluations demonstrate that CP(BV) is much more efficient than previous similar attempts from the CP community, that it is indeed able to solve the majority of the standard verification benchmarks for bitvectors, and that it already complements the standard SMT approaches on several crucial (and industry-relevant) aspects, notably in terms of scalability w.r.t. bit-width, theory combination or intricate mix of non-linear arithmetic and bitwise operators. This work paves the way toward building competitive CP-based verification-oriented solvers.
For the entire collection see [Zbl 1364.68017].

MSC:
68T20 Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.)
90C27 Combinatorial optimization
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