Automatic generation of invariants and intermediate assertions
Theoretical Computer Science - Special issue: principles and practice of constraint programming
Journal of the ACM (JACM)
Automatic Generation of Invariants
Formal Methods in System Design - Special issue on The First Federated Logic Conference (FLOC'96), part II
Automatic discovery of linear restraints among variables of a program
POPL '78 Proceedings of the 5th ACM SIGACT-SIGPLAN symposium on Principles of programming languages
A Technique for Invariant Generation
TACAS 2001 Proceedings of the 7th International Conference on Tools and Algorithms for the Construction and Analysis of Systems
Text classification using string kernels
The Journal of Machine Learning Research
Subword histories and Parikh matrices
Journal of Computer and System Sciences
Subword conditions and subword histories
Information and Computation
Injectivity of the Parikh Matrix Mappings Revisited
Fundamenta Informaticae - SPECIAL ISSUE ON TRAJECTORIES OF LANGUAGE THEORY Dedicated to the memory of Alexandru Mateescu
Automatic verification of real-time systems with rich data: an overview
TAMC'12 Proceedings of the 9th Annual international conference on Theory and Applications of Models of Computation
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We introduce subsequence invariants, which characterize the behavior of a concurrent system in terms of the occurrences of synchronization events. Unlike state invariants, which refer to the state variables of the system, subsequence invariants are defined over auxiliary counter variables that reflect how often the event sequences from a given set have occurred so far. A subsequence invariant is a linear constraint over the possible counter values. We allow every occurrence of a subsequence to be interleaved arbitrarily with other events. As a result, subsequence invariants are preserved when a given process is composed with additional processes. Subsequence invariants can therefore be computed individually for each process and then be used to reason about the full system. We present an efficient algorithm for the synthesis of subsequence invariants. Our construction can be applied incrementally to obtain a growing set of invariants given a growing set of event sequences.