Symbolic Boolean manipulation with ordered binary-decision diagrams
ACM Computing Surveys (CSUR)
A new solution of Dijkstra's concurrent programming problem
Communications of the ACM
A unified approach to global program optimization
POPL '73 Proceedings of the 1st annual ACM SIGACT-SIGPLAN symposium on Principles of programming languages
POPL '77 Proceedings of the 4th ACM SIGACT-SIGPLAN symposium on Principles of programming languages
Principles of Program Analysis
Principles of Program Analysis
Systematic design of program analysis frameworks
POPL '79 Proceedings of the 6th ACM SIGACT-SIGPLAN symposium on Principles of programming languages
Nordic Journal of Computing
Weighted pushdown systems and their application to interprocedural dataflow analysis
Science of Computer Programming - Special issue: Static analysis symposium (SAS 2003)
Compilers: Principles, Techniques, and Tools (2nd Edition)
Compilers: Principles, Techniques, and Tools (2nd Edition)
Principles of Model Checking (Representation and Mind Series)
Principles of Model Checking (Representation and Mind Series)
Explicit Versus Symbolic Algorithms for Solving ALFP Constraints
Electronic Notes in Theoretical Computer Science (ENTCS)
On galois connections and soft computing
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
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We generalise Galois connections from complete lattices to flow algebras. Flow algebras are algebraic structures that are less restrictive than idempotent semirings in that they replace distributivity with monotonicity and dispense with the annihilation property; therefore they are closer to the approach taken by Monotone Frameworks and other classical analyses. We present a generic framework for static analysis based on flow algebras and program graphs. Program graphs are often used in Model Checking to model concurrent and distributed systems. The framework allows to induce new flow algebras using Galois connections such that correctness of the analyses is preserved. The approach is illustrated for a mutual exclusion algorithm.