Model checking and abstraction
ACM Transactions on Programming Languages and Systems (TOPLAS)
A unifying view of abstract domain design
ACM Computing Surveys (CSUR)
Optimal domains for disjunctive abstract interpretation
Science of Computer Programming - Special issue on the 6th European symposium on programming
Model checking
Making abstract interpretations complete
Journal of the ACM (JACM)
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 '77 Proceedings of the 4th ACM SIGACT-SIGPLAN symposium on Principles of programming languages
Systematic design of program analysis frameworks
POPL '79 Proceedings of the 6th ACM SIGACT-SIGPLAN symposium on Principles of programming languages
Refining and Compressing Abstract Domains
ICALP '97 Proceedings of the 24th International Colloquium on Automata, Languages and Programming
Incompleteness, Counterexamples, and Refinements in Abstract Model-Checking
SAS '01 Proceedings of the 8th International Symposium on Static Analysis
Construction of Abstract State Graphs with PVS
CAV '97 Proceedings of the 9th International Conference on Computer Aided Verification
Experience with Predicate Abstraction
CAV '99 Proceedings of the 11th International Conference on Computer Aided Verification
Counterexample-Guided Abstraction Refinement
CAV '00 Proceedings of the 12th International Conference on Computer Aided Verification
Counterexample-guided abstraction refinement for symbolic model checking
Journal of the ACM (JACM)
Principles of Model Checking (Representation and Mind Series)
Principles of Model Checking (Representation and Mind Series)
Transforming Abstract Interpretations by Abstract Interpretation
SAS '08 Proceedings of the 15th international symposium on Static Analysis
Fixpoint-guided abstraction refinements
SAS'07 Proceedings of the 14th international conference on Static Analysis
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In static analysis, approximation is typically encoded by abstract domains, providing systematic guidelines for specifying approximate semantic functions and precision assessments. However, it may well happen that an abstract domain contains redundant information for the specific purpose of approximating a given semantic function modeling some behavior of a system. This paper introduces Example-Guided Abstraction Simplification (EGAS), a methodology for simplifying abstract domains, i.e. removing abstract values from them, in a maximal way while retaining exactly the same approximate behavior of the system under analysis. We show that, in abstract model checking and predicate abstraction, EGAS provides a simplification paradigm of the abstract state space that is guided by examples, meaning that it preserves spuriousness of examples (i.e., abstract paths). In particular, we show how EGAS can be integrated with the well-known CEGAR (CounterExample-Guided Abstraction Refinement) methodology.