|
ABSTRACT
Automated verification is a technique for establishing if certain properties, usually expressed in temporal logic, hold for a system model. The model can be defined using a high-level formalism or extracted directly from software using methods such as abstract interpretation. The verification proceeds through exhaustive exploration of the state-transition graph of the model and is therefore more powerful than testing. Quantitative verification is an analogous technique for establishing quantitative properties of a system model, such as the probability of battery power dropping below minimum, the expected time for message delivery and the expected number of messages lost before protocol termination. Models analysed through this method are typically variants of Markov chains, annotated with costs and rewards that describe resources and their usage during execution. Properties are expressed in temporal logic extended with probabilistic and reward operators. Quantitative verification involves a combination of a traversal of the state-transition graph of the model and numerical computation. This paper gives a brief overview of current research in quantitative verification, concentrating on the potential of the method and outlining future challenges. The modelling approach is described and the usefulness of the methodology illustrated with an example of a real-world protocol standard - Bluetooth device discovery - that has been analysed using the PRISM model checker (www.prismmodelchecker.org).
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
| |
1
|
H. Aljazzar and S. Leue. Extended directed search for probabilistic timed reachability. In FORMATS'06, volume 4202 of LNCS, pages 33--51. Springer, 2006.
|
| |
2
|
|
| |
3
|
|
| |
4
|
Christel Baier , Edmund M. Clarke , Vassili Hartonas-Garmhausen , Marta Z. Kwiatkowska , Mark Ryan, Symbolic Model Checking for Probabilistic Processes, Proceedings of the 24th International Colloquium on Automata, Languages and Programming, p.430-440, July 07-11, 1997
|
| |
5
|
|
| |
6
|
|
| |
7
|
|
| |
8
|
|
| |
9
|
S. Cattani, R. Segala, M. Kwiatkowska, and G. Norman. Stochastic transition systems for continuous state spaces and non-determinism. In Proc. FOSSACS'05, volume 3441 of LNCS, pages 125--139. Springer Verlag, 2005.
|
 |
10
|
|
| |
11
|
C. Courcoubetis and M. Yannakakis. Verifying temporal properties of finite state probabilistic programs. In Proc. FOCS'88, pages 338--345. IEEE CS Press, 1988.
|
 |
12
|
|
| |
13
|
|
| |
14
|
|
| |
15
|
H. Hansson and B. Jonsson. A logic for reasoning about time and reliability. Formal Aspects of Computing, 6(5):512--535, 1994.
|
| |
16
|
J. Heath, M. Kwiatkowska, G. Norman, D. Parker, and O. Tymchyshyn. Probabilistic model checking of complex biological pathways. In C. Priami, editor, Proc. Computational Methods in Systems Biology (CMSB'06), volume 4210 of Lecture Notes in Bioinformatics, pages 32--47. Springer Verlag, 2006.
|
| |
17
|
T. Hérault, R. Lassaigne, F. Magniette, and S. Peyronnet. Approximate probabilistic model checking. In Proc. VMCAI'04, volume 2937 of LNCS. Springer, 2004.
|
| |
18
|
A. Hinton, M. Kwiatkowska, G. Norman, and D. Parker. PRISM: A tool for automatic verification of probabilistic systems. In Proc. TACAS'06, volume 3920 of LNCS, pages 441--444. Springer, 2006.
|
| |
19
|
J. Kemeny, J. Snell, and A. Knapp. Denumerable Markov Chains. D. Van Nostrand Company, 1966.
|
| |
20
|
|
| |
21
|
|
 |
22
|
|
| |
23
|
M. Kwiatkowska, G. Norman, and D. Parker. Quantitative analysis with the probabilistic model checker PRISM. Electronic Notes in Theoretical Computer Science, 153(2):5--31, 2005.
|
| |
24
|
M. Kwiatkowska, G. Norman, and D. Parker. Stochastic model checking. In Formal Methods for the Design of Computer, Communication and Software Systems: Performance Evaluation (SFM'07), volume 4486 of LNCS (Tutorial Volume), pages 220--270. Springer, 2007.
|
| |
25
|
|
| |
26
|
M. Kwiatkowska, G. Norman, D. Parker, and J. Sproston. Verification of real-time probabilistic systems. In S. Mertz and N. Navet, editors, Modelling and Verification of Real-Time Systems, 2007. To appear.
|
| |
27
|
|
| |
28
|
|
| |
29
|
|
| |
30
|
PRISM web site: www.prismmodelchecker.org.
|
| |
31
|
|
| |
32
|
J. Rutten, M. Kwiatkowska, G. Norman, and D. Parker. Mathematical Techniques for Analyzing Concurrent and Probabilistic Systems, P. Panangaden and F. van Breugel (eds.), volume 23 of CRM Monograph Series. AMS, 2004.
|
| |
33
|
K. Sen, M. Viswanathan, and G. Agha. On statistical model checking of stochastic systems. In Proc. CAV'05, volume 3576 of LNCS, pages 266--280. Springer, 2005.
|
| |
34
|
W. J. Stewart. Introduction to the Numerical Solution of Markov Chains. Princeton, 1994.
|
| |
35
|
M. Vardi. Automatic verification of probabilistic concurrent finite state programs. In Proc. 26th Annual Symposium on Foundations of Computer Science (FOCS'85), pages 327--338. IEEE CS Press, 1985.
|
| |
36
|
|
| |
37
|
|
|