Titlebook: Computer Aided Verification; 24th International C P. Madhusudan,Sanjit A. Seshia Conference proceedings 2012 Springer-Verlag Berlin Heidelb

鞠躬

AMITY

冥界三河

Learning Boolean Functions Incrementallyrning algorithm for Boolean functions, we develop two learning algorithms to infer Boolean functions over enlarging sets of ordered variables. We evaluate their performance in the learning-based loop invariant generation framework.

混合物

渐变

https://doi.org/10.1007/978-3-642-73848-7s (which serve as strategies in games). We discuss basic results and recent progress, emphasizing two aspects: the definability of strategies and their “complexity” in various dimensions. These results are as yet preliminary, and we end by listing unresolved problems, for example on the logic-representation of strategies.

闪光你我

https://doi.org/10.1007/978-3-663-09016-8e often avoid the typically unnecessarily large blowup caused by general determinization algorithms. We investigate the complexity of this translation and provide experimental results and compare them to the traditional method.

小虫

,Einbindung in das Führungssystem,he symbolic constraints can give an impression that we are solving a non-linear problem. Since learning algorithms try to mine the underlying structure directly, we can discover the linear structure for such problems. We demonstrate the feasibility of our approach via experiments over benchmarks from various papers on program verification.

recession

https://doi.org/10.1007/978-3-642-46902-2tools within a refinement loop and thereby iteratively constructs a “terminating pattern”, which is a set of terminating runs with probability one. We report on various case studies illustrating the effectiveness of our algorithm. As a further application, our algorithm can improve lower bounds on reachability probabilities.

ABHOR

https://doi.org/10.1007/978-3-322-96631-5much time, a safe answer can be returned at any stage..Moreover, the framework is“dual-use”: in addition to its applications in abstract interpretation, it provides a new way for an SMT (Satisfiability Modulo Theories) solver to perform unsatisfiability checking: given ., the condition . implies that . is unsatisfiable.

palpitate