Effective Approaches to Abstraction Refinement for Automatic Software Verification

This thesis presents various techniques that aim at enabling more effective and more efficient approaches for automatic software verification. After a brief motivation why automatic software verification is getting ever more relevant, we continue with detailing the formalism used in this thesis and on the concepts it is built on. We then describe the design and implementation of the value analysis, an analysis for automatic software verification that tracks state information concretely. From a thorough evaluation based on well over 4 000 verification tasks from the latest edition of the International Competition on Software Verification (SV-COMP), we learn that this plain value analysis leads to an efficient verification process for many verification tasks, but at the same time, fails to solve other verification tasks due to state-space explosion. From this insight we infer that some form of abstraction technique must be added to the value analysis in order to also allow the successful verification of large and complex verification tasks. As a solution, we propose to incorporate counterexample-guided abstraction refinement (CEGAR) and interpolation into the value domain. To this end, we design a novel interpolation procedure, that extracts from infeasible counterexamples interpolants for the value domain, allowing to form a precision strong enough to exclude these infeasible counterexamples, and to make progress in the CEGAR loop. We then describe several optimizations and extensions to these concepts, such that the value analysis with CEGAR becomes competitive for automatic software verification. As the next step, we combine the value analysis with CEGAR with a predicate analysis, to obtain a more precise and efficient composite analysis based on CEGAR. This composite analysis is indeed on a par with the world’s leading software verification tools, as witnessed by the results of SV-COMP’13 where this approach achieved the 2nd place in the overall ranking. After having available competitive CEGAR-based analyses for the value domain, the predicate domain, and the combination thereof, we then turn our attention to techniques that have the goal to make all these CEGAR-based approaches more successful. Our first novel idea in this regard is based on the concept of infeasible sliced prefixes, which allow the computation of different precisions from a single infeasible counterexample. This adds choice to the CEGAR loop, while without this enhancement, no choice for a specific precision, i. e., a specific refinement, is possible.