Non-linear loop invariant generation using Gröbner bases
Proceedings of the 31st ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Abstraction, Refinement And Proof For Probabilistic Systems (Monographs in Computer Science)
Abstraction, Refinement And Proof For Probabilistic Systems (Monographs in Computer Science)
Linear-invariant generation for probabilistic programs: automated support for proof-based methods
SAS'10 Proceedings of the 17th international conference on Static analysis
Probabilistic reachability for parametric Markov models
International Journal on Software Tools for Technology Transfer (STTT) - SPIN 2009
PRISM 4.0: verification of probabilistic real-time systems
CAV'11 Proceedings of the 23rd international conference on Computer aided verification
Floyd--hoare logic for quantum programs
ACM Transactions on Programming Languages and Systems (TOPLAS)
Probabilistic relational reasoning for differential privacy
POPL '12 Proceedings of the 39th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages
PASS: abstraction refinement for infinite probabilistic models
TACAS'10 Proceedings of the 16th international conference on Tools and Algorithms for the Construction and Analysis of Systems
On the complexity of the equivalence problem for probabilistic automata
FOSSACS'12 Proceedings of the 15th international conference on Foundations of Software Science and Computational Structures
APEX: an analyzer for open probabilistic programs
CAV'12 Proceedings of the 24th international conference on Computer Aided Verification
Probabilistic relational hoare logics for computer-aided security proofs
MPC'12 Proceedings of the 11th international conference on Mathematics of Program Construction
Operational Versus Weakest Precondition Semantics for the Probabilistic Guarded Command Language
QEST '12 Proceedings of the 2012 Ninth International Conference on Quantitative Evaluation of Systems
Hi-index | 0.00 |
Prinsys (pronounced "princess") is a new software-tool for pr obabilistic in variant sy nthesis . In this paper we discuss its implementation and improvements of the methodology which was set out in previous work. In particular we have substantially simplified the method and generalised it to non-linear programs and invariants. Prinsys follows a constraint-based approach. A given parameterised loop annotation is speculatively placed in the program. The tool returns a formula that captures precisely the invariant instances of the given candidate. Our approach is sound and complete. Prinsys's applicability is evaluated on several examples. We believe the tool contributes to the successful analysis of sequential probabilistic programs with infinite-domain variables and parameters.