Learning regular sets from queries and counterexamples
Information and Computation
Tentative steps toward a development method for interfering programs
ACM Transactions on Programming Languages and Systems (TOPLAS)
In transition from global to modular temporal reasoning about programs
Logics and models of concurrent systems
Inference of finite automata using homing sequences
Information and Computation
Introduction To Automata Theory, Languages, And Computation
Introduction To Automata Theory, Languages, And Computation
Checking Safety Properties Using Induction and a SAT-Solver
FMCAD '00 Proceedings of the Third International Conference on Formal Methods in Computer-Aided Design
On the Competeness of Compositional Reasoning
CAV '00 Proceedings of the 12th International Conference on Computer Aided Verification
Breaking up is hard to do: an investigation of decomposition for assume-guarantee reasoning
Proceedings of the 2006 international symposium on Software testing and analysis
Proofs of Networks of Processes
IEEE Transactions on Software Engineering
SAT-based Induction for Temporal Safety Properties
Electronic Notes in Theoretical Computer Science (ENTCS)
Refining interface alphabets for compositional verification
TACAS'07 Proceedings of the 13th international conference on Tools and algorithms for the construction and analysis of systems
Learning assumptions for compositional verification
TACAS'03 Proceedings of the 9th international conference on Tools and algorithms for the construction and analysis of systems
A fast linear-arithmetic solver for DPLL(T)
CAV'06 Proceedings of the 18th international conference on Computer Aided Verification
Symbolic compositional verification by learning assumptions
CAV'05 Proceedings of the 17th international conference on Computer Aided Verification
An analysis of SAT-based model checking techniques in an industrial environment
CHARME'05 Proceedings of the 13 IFIP WG 10.5 international conference on Correct Hardware Design and Verification Methods
Learning-based symbolic assume-guarantee reasoning with automatic decomposition
ATVA'06 Proceedings of the 4th international conference on Automated Technology for Verification and Analysis
Regular inference for state machines with parameters
FASE'06 Proceedings of the 9th international conference on Fundamental Approaches to Software Engineering
Automated assumption generation for compositional verification
Formal Methods in System Design
Learning to divide and conquer: applying the L* algorithm to automate assume-guarantee reasoning
Formal Methods in System Design
Verification of evolving software via component substitutability analysis
Formal Methods in System Design
Automatic symbolic compositional verification by learning assumptions
Formal Methods in System Design
Automated Assume-Guarantee Reasoning by Abstraction Refinement
CAV '08 Proceedings of the 20th international conference on Computer Aided Verification
Learning Minimal Separating DFA's for Compositional Verification
TACAS '09 Proceedings of the 15th International Conference on Tools and Algorithms for the Construction and Analysis of Systems: Held as Part of the Joint European Conferences on Theory and Practice of Software, ETAPS 2009,
Comparing learning algorithms in automated assume-guarantee reasoning
ISoLA'10 Proceedings of the 4th international conference on Leveraging applications of formal methods, verification, and validation - Volume Part I
Automated assume-guarantee reasoning for omega-regular systems and specifications
Innovations in Systems and Software Engineering
Automated assume-guarantee reasoning through implicit learning
CAV'10 Proceedings of the 22nd international conference on Computer Aided Verification
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A recent approach to automated assume-guarantee reasoning (AGR) for concurrent systems relies on computing environment assumptions for components using the L* algorithm for learning regular languages.While this approach has been investigated extensively for message passing systems, it still remains a challenge to scale the technique to large shared memory systems, mainly because the assumptions have an exponential communication alphabet size. In this paper, we propose a SAT-based methodology that employs both induction and interpolation to implement automated AGR for shared memory systems. The method is based on a new lazy approach to assumption learning, which avoids an explicit enumeration of the exponential alphabet set during learning by using symbolic alphabet clustering and iterative counterexample-driven localized partitioning. Preliminary experimental results on benchmarks in Verilog and SMV are encouraging and show that the approach scales well in practice.