Advanced Model Checking
Graduate course - Winter Term 06/07
Lehrstuhl f?r Informatik 2
Schedule
Type
Time
Place
Start
Lecturer
Exam
Thu 9:00 - 11:00
AH1
15.02.2007
-
Exam
Information regarding the exam:
- The exam will take place on Thursday, Feb. 15th. from 9:00 until 11:00 in AH 1
- The only allowed material is a copy of the slides
New!
- You would like to discuss some course related topics
- You have question to the teaching staff (instead of using e-mail)
- You have found a typo or a mistake in the lecture notes or course slides
- 13.11.2006, 30.11.2006 -- No lecture
- 09.11.2006 -- Possibly no lecture, stay tuned!
Motivation and background
This course is concerned with model checking, an automated technique to verify properties of hardware and software systems. Whereas the focus of the course Model Checking is on the elementary techniques of model checking, this course is focused on two main topics: advancing current model-checking technology, and, on the other hand, model-checking techniques for quantitative system aspects.
More concretely, the course will 紡fter a summary of the main model-checking techniques for LTL and CTL? treat state space reduction techniques. This ranges from algorithms to minimise state-space representations using equivalences and pre-orders (bisimulations and simulation relations), techniques to avoid representing all possible interleaving of concurrent components (partial-order reduction) and data structures for the succinct representation of state spaces (e.g., binary decision diagrams).
In the second part of the course, models and algorithms are treated for the verification of timed properties, such as ``is it possible that the system will crash within 30 seconds'', and properties that involve random phenomena (e.g., ``the probability to reach a bad state within 44 minutes is below 0.0001''). Models such as timed automata, their infinite-state semantics, and finite abstractions thereof will be treated. This is complemented by a treatment of algorithms for checking timed CTL. This results in an effective framework that is used for checking real-time properties of embedded systems, communication protocols, and so on.
Probabilistic models are the key to model random phenomena as they occur in distributed algorithms that use randomisation to break the symmetry between processes, or to reason about quality of service parameters such as dependability, performance, and survivability. This course will deal with the basic algorithms and logics for verifying properties of fully probabilistic models such as Markov chains, and (if time permits) models that also exhibit nondeterminism (Markov decision processes).
More concretely, the course will 紡fter a summary of the main model-checking techniques for LTL and CTL? treat state space reduction techniques. This ranges from algorithms to minimise state-space representations using equivalences and pre-orders (bisimulations and simulation relations), techniques to avoid representing all possible interleaving of concurrent components (partial-order reduction) and data structures for the succinct representation of state spaces (e.g., binary decision diagrams).
In the second part of the course, models and algorithms are treated for the verification of timed properties, such as ``is it possible that the system will crash within 30 seconds'', and properties that involve random phenomena (e.g., ``the probability to reach a bad state within 44 minutes is below 0.0001''). Models such as timed automata, their infinite-state semantics, and finite abstractions thereof will be treated. This is complemented by a treatment of algorithms for checking timed CTL. This results in an effective framework that is used for checking real-time properties of embedded systems, communication protocols, and so on.
Probabilistic models are the key to model random phenomena as they occur in distributed algorithms that use randomisation to break the symmetry between processes, or to reason about quality of service parameters such as dependability, performance, and survivability. This course will deal with the basic algorithms and logics for verifying properties of fully probabilistic models such as Markov chains, and (if time permits) models that also exhibit nondeterminism (Markov decision processes).
Contents
- Summary of LTL and CTL model checking
- Equivalences and abstraction
- Partial-order reduction techniques
- Binary decision diagrams
- Timed automata
- Model checking timed CTL
- Probabilistic systems
- Model checking probabilistic CTL
Prerequisites
Basic knowledge of automata theory, complexity theory, and data structures and algorithms. The course is a follow-up course of Model Checking. It is highly recommended to have basic knowledge of model checking, although this is not mandatory.
Language
The lecture will be given in English.
All course material (i.e., lecture notes and slides) will be in English.
All course material (i.e., lecture notes and slides) will be in English.
Exercises
Exercises can be worked on in groups of at most two students. To achieve a certificate to this course, at least half of the exercises has to be reasonably dealt with and a final exam has to be passed.
The exercise sheets will be issued weekly.
The exercise sheets will be issued weekly.
Overhead transparencies
All overhead transparencies that are used during the lecture will be made available here.
Date
Lecture
Subject
Slides
Exercise
Solutions
Nov 6
5
Simulation Quotienting(2)
Links
- The Advanced Model Checking Forum
- Mode Checking Tools:
Literature
Additional literature can be found in:
- J. Rutten, M. Kwiatkowska, G. Norman and D. Parker: Mathematical Techniques for Analyzing Concurrent and Probabilistic Systems, Volume 23 of CRM Monograph Series. American Mathematical Society, P. Panangaden and F. van Breugel (eds.), March 2004.
- M. Huth and M.D. Ryan: Logic in Computer Science -- Modelling and Reasoning about Systems, Cambridge University Press, 2nd edition, 2004
- K. Schneider: Verification of Reactive Systems, Springer-Verlag, Texts in Theoretical Computer Science. An EATCS Series, 2004
- J.-P. Katoen: Concepts, Algorithms and Tools for Model Checking, Erlangen: Institut f?r Mathematische Maschinen und Datenverarbeitung, 1999
- E.M. Clarke, O. Grumberg, D.A. Peled: Model Checking, MIT Press, 1999
- K.L. McMillan: Symbolic Model Checking, Kluwer Academic, 1993