HybridTiger: Hybrid Model Checking and Domination-based Partitioning for Efficient Multi-Goal Test-Suite Generation (Competition Contribution)

Abstract

In theory, software model checkers are well-suited for automated test-case generation. The idea is to perform (non-)reachability queries for the test goals and extract test cases from resulting counterexamples. However, in case of realistic programs, even simple coverage criteria (e.g., branch coverage) force model checkers to deal with several hundreds or even thousands of test goals. Processing each of these test goals in isolation with model checking techniques does not scale. Therefore, our tool HybridTiger builds on recent ideas on multi-property verification. However, since every additional property (i.e., test goal) reduces the model checker’s abstraction possibilities, we split the set of all test goals into different partitions. In Test-Comp 2019, we applied a random partitioning strategy and used predicate analysis as model checking technique. In Test-Comp 2020, we improved our technique in two ways. First, we exploit domination information among control-flow locations in our partitioning strategy to group test goals being located on (preferably) similar paths. Second, we account to inherent weaknesses of the predicate analysis by applying a hybrid software model-checking approach that switches between explicit model checking and predicate-based model checking on-the-fly. Our tool HybridTiger is integrated into the software analysis framework CPAchecker.