The SLAM project: debugging system software via static analysis
POPL '02 Proceedings of the 29th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
POPL '02 Proceedings of the 29th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
POPL '77 Proceedings of the 4th ACM SIGACT-SIGPLAN symposium on Principles of programming languages
Parametric shape analysis via 3-valued logic
ACM Transactions on Programming Languages and Systems (TOPLAS)
Principles of Program Analysis
Principles of Program Analysis
TVLA: A System for Implementing Static Analyses
SAS '00 Proceedings of the 7th International Symposium on Static Analysis
Counterexample-Guided Abstraction Refinement
CAV '00 Proceedings of the 12th International Conference on Computer Aided Verification
Proceedings of the 31st ACM SIGPLAN-SIGACT symposium on Principles of programming languages
CAV'06 Proceedings of the 18th international conference on Computer Aided Verification
Abstraction refinement via inductive learning
CAV'05 Proceedings of the 17th international conference on Computer Aided Verification
Counterexample driven refinement for abstract interpretation
TACAS'06 Proceedings of the 12th international conference on Tools and Algorithms for the Construction and Analysis of Systems
Refining Abstract Interpretation-Based Static Analyses with Hints
APLAS '09 Proceedings of the 7th Asian Symposium on Programming Languages and Systems
A forward-backward abstraction refinement algorithm
VMCAI'08 Proceedings of the 9th international conference on Verification, model checking, and abstract interpretation
Loop summarization using state and transition invariants
Formal Methods in System Design
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Counterexample-guided abstraction refinement (CEGAR) is a powerful technique to scale automatic program analysis techniques to large programs. However, so far it has been used primarily formodel checking in the context of predicate abstraction.We formalize CEGAR for general powerset domains. If a spurious abstract counterexample needs to be removed through abstraction refinement, there are often several choices, such as which program location(s) to refine, which abstract domain(s) to use at different locations, and which abstract values to compute. We define several plausible preference orderings on abstraction refinements, such as refining as "late" as possible and as "coarse" as possible. We present generic algorithms for finding refinements that are optimal with respect to the different preference orderings.We also compare the different orderings with respect to desirable properties, including the property if locally optimal refinements compose to a global optimum. Finally, we point out some difficulties with CEGAR for non-powerset domains.