[Zurück]

E. Bartocci, R. Grosu, P. Katsaros, C. Ramakrishnan, S. Smolka:
"Model Repair for Probabilistic Systems";
Vortrag: 17th International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS), Saarbrücken, Germany; 26.03.2011 - 03.04.2011; in: "Proc. of 17th International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS)", LNCS / Springer, vol. 6605 (2011), ISBN: 978-3-642-19834-2; S. 326 - 340.

We introduce the problem of Model Repair for Probabilistic Systems as follows. Given a probabilistic system M and a probabilistic temporal logic formula φ such that M fails to satisfy φ, the Model Repair problem is to find an M′ that satisfies φ and differs from M only in the transition flows of those states in M that are deemed controllable. Moreover, the cost associated with modifying M´s transition flows to obtain M′ should be minimized. Using a new version of parametric probabilistic model checking, we show how the Model Repair problem can be reduced to a nonlinear optimization problem with a minimal-cost objective function, thereby yielding a solution technique. We demonstrate the practical utility of our approach by applying it to a number of significant case studies, including a DTMC reward model of the Zeroconf protocol for assigning IP addresses, and a CTMC model of the highly publicized Kaminsky DNS cache-poisoning attack.

http://dx.doi.org/10.1007/978-3-642-19835-9_30