Optimizing parallel Korat using invalid ranges

Constraint-based input generation enables systematic testing for effective bug finding, but requires exploration of very large spaces of candidate inputs. This paper introduces a novel approach to optimize input generation using Korat – a solver for constraints written as imperative predicates in Java – when Korat is executed more than once for the same constraint solving problem. Our key insight is that in certain application scenarios the Korat search over the same state space and constraint is repeated across separate runs of Korat, and an earlier run can be summarized to optimize a later run. We introduce invalid ranges to represent parts of the exploration space that do not contain any valid inputs but must be explicitly explored by Korat. Our approach directly prunes these parts in a future run of Korat over the same search problem. We develop our approach for two settings: a sequential setting where the Korat search is run using one worker (i.e., processing unit), and a parallel setting where the search is distributed to several workers. In the parallel setting, we build on a previous technique for parallel Korat, namely SEQ-ON, and integrate invalid ranges with it. Experimental evaluation using 6 subjects show that our approach achieves: in the sequential setting, a speedup of up to 2.82X over sequential Korat (in comparison, SEQ-ON does not provide any speedup in the sequential setting); and in the distributed setting, using up to 32 workers, a speedup of up to 38.84X over sequential Korat (using 1 worker), and up to 3.04X over SEQ-ON in terms of total execution time across the workers.