Learning regular sets from queries and counterexamples
Information and Computation
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
Computer-aided verification of coordinating processes: the automata-theoretic approach
Computer-aided verification of coordinating processes: the automata-theoretic approach
Dynamically discovering likely program invariants to support program evolution
Proceedings of the 21st international conference on Software engineering
FORTE XII / PSTV XIX '99 Proceedings of the IFIP TC6 WG6.1 Joint International Conference on Formal Description Techniques for Distributed Systems and Communication Protocols (FORTE XII) and Protocol Specification, Testing and Verification (PSTV XIX)
TACAS '02 Proceedings of the 8th International Conference on Tools and Algorithms for the Construction and Analysis of Systems
Assumption Generation for Software Component Verification
Proceedings of the 17th IEEE international conference on Automated software engineering
Counterexample-guided abstraction refinement for symbolic model checking
Journal of the ACM (JACM)
Synthesis of interface specifications for Java classes
Proceedings of the 32nd ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Assume-Guarantee Reasoning for Deadlock
FMCAD '06 Proceedings of the Formal Methods in Computer Aided Design
Proofs of Networks of Processes
IEEE Transactions on Software Engineering
Regular Model Checking Using Inference of Regular Languages
Electronic Notes in Theoretical Computer Science (ENTCS)
Optimized L*-based assume-guarantee reasoning
TACAS'07 Proceedings of the 13th international conference on Tools and algorithms for the construction and analysis of systems
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
The ComFoRT reasoning framework
CAV'05 Proceedings of the 17th international conference on Computer Aided Verification
Automated assume-guarantee reasoning for simulation conformance
CAV'05 Proceedings of the 17th 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 effective framework for assume-guarantee verification of evolving component-based software
Proceedings of the joint international and annual ERCIM workshops on Principles of software evolution (IWPSE) and software evolution (Evol) workshops
A Minimized Assumption Generation Method for Component-Based Software Verification
ICTAC '09 Proceedings of the 6th International Colloquium on Theoretical Aspects of Computing
Variants of LTL query checking
HVC'10 Proceedings of the 6th international conference on Hardware and software: verification and testing
Symbolic learning of component interfaces
SAS'12 Proceedings of the 19th international conference on Static Analysis
Hybrid learning: interface generation through static, dynamic, and symbolic analysis
Proceedings of the 2013 International Symposium on Software Testing and Analysis
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The learning-based automated Assume---Guarantee reasoning paradigm has been applied in the last few years for the compositional verification of concurrent systems. Specifically, L* has been used for learning the assumption, based on strings derived from counterexamples, which are given to it by a model-checker that attempts to verify the Assume---Guarantee rules. We suggest three optimizations to this paradigm. First, we derive from each counterexample multiple strings to L*, rather than a single one as in previous approaches. This small improvement saves candidate queries and hence model-checking runs. Second, we observe that in existing instances of this paradigm, the learning algorithm is coupled weakly with the teacher. Thus, the learner completely ignores the details of the internal structure of the system and specification being verified, which are available already to the teacher. We suggest an optimization that uses this information in order to avoid many unnecessary membership queries (it reduces the number of such queries by more than an order of magnitude). Finally, we develop a method for minimizing the alphabet used by the assumption, which reduces the size of the assumption and the number of queries required to construct it. We present these three optimizations in the context of verifying trace containment for concurrent systems composed of finite state machines. We have implemented our approach in the ComFoRT tool, and experimented with real-life examples. Our results exhibit an average speedup of between 4 to 11 times, depending on the Assume---Guarantee rule used and the set of activated optimizations.