scholarly journals Pegasus: A Framework for Sound Continuous Invariant Generation

2019 ◽  
pp. 138-157 ◽  
Author(s):  
Andrew Sogokon ◽  
Stefan Mitsch ◽  
Yong Kiam Tan ◽  
Katherine Cordwell ◽  
André Platzer
Keyword(s):  
Invariant Generation

scholarly journals Improved Invariant Generation for Tvoc

2007 ◽  
Vol 176 (3) ◽  
pp. 21-35
Author(s):  
Yi Fang ◽  
Lenore D. Zuck
Keyword(s):  
Invariant Generation

scholarly journals Invariants and Robustness of BIP Models

10.29007/prxp ◽  
2018 ◽  
Author(s):  
Jan Olaf Blech ◽  
Thanh-Hung Nguyen ◽  
Michael Perin
Keyword(s):  
Embedded Systems ◽  
Higher Order ◽  
Theorem Prover ◽  
Important Goal ◽  
Invariant Generation ◽  
Execution Traces ◽  
Verification Tool ◽  
Verification Tools ◽  
Generation Mechanisms ◽  
Natural Way

In this paper we present on-going work addressing the problem of automatically generating realistic and guaranteed correct invariants. Since invariant generation mechanisms are error-prone, after the computation of invariants by a verification tool, we formally prove that the generated invariants are indeed invariants of the considered systems using a higher-order theorem prover and automated techniques. We regard invariants for BIP models. BIP (behavior, interaction, priority) is a language for specifying asynchronous component based systems. Proving that an invariant holds often requires an induction on possible system execution traces. For this reason, apart from generating invariants that precisely capture a system’s behavior, inductiveness of invariants is an important goal. We establish a notion of robust BIP models. These can be automatically constructed from our original non-robust BIP models and over-approximate their behavior. We motivate that invariants of robust BIP models capture the behavior of systems in a more natural way than invariants of corresponding non-robust BIP models. Robust BIP models take imprecision due to values delivered by sensors into account. Invariants of robust BIP models tend to be inductive and are also invariants of the original non-robust BIP model. Therefore they may be used by our verification tools and it is easy to show their correctness in a higher-order theorem prover. The presented work is developed to verify the results of a deadlock-checking tool for embedded systems after their computations. Therewith, we gain confidence in the provided analysis results.

scholarly journals Invariant Generation for Parametrized Systems Using Self-reflection

2012 ◽  
pp. 146-163 ◽  
Author(s):  
Alejandro Sanchez ◽  
Sriram Sankaranarayanan ◽  
César Sánchez ◽  
Bor-Yuh Evan Chang
Keyword(s):  
Self Reflection ◽  
Invariant Generation ◽  
Parametrized Systems

Linear-Invariant Generation for Probabilistic Programs:

2010 ◽  
pp. 390-406 ◽  
Author(s):  
Joost-Pieter Katoen ◽  
Annabelle K. McIver ◽  
Larissa A. Meinicke ◽  
Carroll C. Morgan
Keyword(s):  
Invariant Generation ◽  
Linear Invariant ◽  
Probabilistic Programs

Invariant Generation

2007 ◽  
pp. 311-346
Keyword(s):  
Invariant Generation

scholarly journals A Technique for Invariant Generation

2001 ◽  
pp. 113-127 ◽  
Author(s):  
A. Tiwari ◽  
H. Rueß ◽  
H. Saïdi ◽  
N. Shankar
Keyword(s):  
Invariant Generation

Decidability of affine solution problems

2019 ◽  
Vol 30 (3) ◽  
pp. 697-714
Author(s):  
Stefan Hetzl ◽  
Sebastian Zivota
Keyword(s):  
Computer Science ◽  
Iteration Procedure ◽  
Loop Invariant ◽  
Affine Spaces ◽  
Invariant Generation ◽  
First Order ◽  
Equivalent Statement

Abstract We present formula equations—first-order formulas with unknowns standing for predicates—as a general formalism for treating certain questions in logic and computer science, like the Auflösungsproblem and loop invariant generation. In the case of the language of affine terms over $\mathbb{Q}$, we translate a quantifier-free formula equation into an equivalent statement about affine spaces over $\mathbb{Q}$, which can then be decided by an iteration procedure.

scholarly journals Invariant Generation through Strategy Iteration in Succinctly Represented Control Flow Graphs

2012 ◽  
Vol 8 (3) ◽  
Author(s):  
Thomas Gawlitza ◽  
David Monniaux
Keyword(s):  
Control Flow ◽  
Invariant Generation ◽  
Flow Graphs
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