Learning Minimal Separating DFA's for Compositional Veri cation

Algorithms for learning a minimal separating DFA of two disjoint regular languages have been proposed and adapted for di erent applications. One of the most important applications is learning minimal contextual assumptions in automated compositional veri cation. We propose in this paper an e cient learning algorithm, called L , that learns and generates a minimal separating DFA. Our algorithm has a quadratic query complexity in the product of sizes of the minimal DFA's for the two input languages. In contrast, the most recent algorithm of Gupta et al. has an exponential query complexity in the sizes of the two DFA's. Moreover, experimental results show that our learning algorithm signi cantly outperforms all existing algorithms on randomly-generated example problems. We describe how our algorithm can be adapted for automated compositional veri cation. The adapted version is evaluated on the LTSA benchmarks and compared with other automated compositional veri cation approaches. The result shows that our algorithm surpasses others in 30 of 49 benchmark problems.