Probabilistic Verification at Runtime for Self-Adaptive Systems

An effective design of effective and efficient self-adaptive systems may rely on several existing approaches. Software models and model checking techniques at run time represent one of them since they support automatic reasoning about such changes, detect harmful configurations, and potentially enable appropriate (self-)reactions. However, traditional model checking techniques and tools may not be applied as they are at run time, since they hardly meet the constraints imposed by on-the-fly analysis, in terms of execution time and memory occupation. For this reason, efficient run-time model checking represents a crucial research challenge.

[1]  Joost-Pieter Katoen,et al.  Discrete-Time Rewards Model-Checked , 2003, FORMATS.

[2]  Jan Johannsen,et al.  Optimal Lower Bounds on Regular Expression Size Using Communication Complexity , 2008, FoSSaCS.

[3]  J. Doob Stochastic processes , 1953 .

[4]  Rajeev Alur,et al.  A Temporal Logic of Nested Calls and Returns , 2004, TACAS.

[5]  Ian Stark,et al.  Free-Algebra Models for the pi-Calculus , 2005, FoSSaCS.

[6]  Joost-Pieter Katoen,et al.  Bisimulation Minimisation Mostly Speeds Up Probabilistic Model Checking , 2007, TACAS.

[7]  Yousef Saad,et al.  Iterative methods for sparse linear systems , 2003 .

[8]  Christel Baier,et al.  Principles of model checking , 2008 .

[9]  Marta Z. Kwiatkowska,et al.  Stochastic Model Checking , 2007, SFM.

[10]  Eila Niemelä,et al.  Survey of reliability and availability prediction methods from the viewpoint of software architecture , 2007, Software & Systems Modeling.

[11]  Lijun Zhang,et al.  Probabilistic reachability for parametric Markov models , 2010, International Journal on Software Tools for Technology Transfer.

[12]  Irina Shklovski,et al.  Guest Editors' Introduction: Urban Computing--Navigating Space and Context , 2006, Computer.

[13]  Jeffrey D. Ullman,et al.  Introduction to Automata Theory, Languages and Computation , 1979 .

[14]  Gregor Engels,et al.  Graph Transformations and Model-Driven Engineering - Essays Dedicated to Manfred Nagl on the Occasion of his 65th Birthday , 2010, Graph Transformations and Model-Driven Engineering.

[15]  Christel Baier,et al.  Partial Order Reduction for Probabilistic Branching Time , 2006, QAPL.

[16]  Carlo Ghezzi,et al.  Further steps towards efficient runtime verification: Handling probabilistic cost models , 2012, 2012 First International Workshop on Formal Methods in Software Engineering: Rigorous and Agile Approaches (FormSERA).

[17]  Lars Grunske,et al.  Specification patterns for probabilistic quality properties , 2008, 2008 ACM/IEEE 30th International Conference on Software Engineering.

[18]  Jeff Magee,et al.  Self-Managed Systems: an Architectural Challenge , 2007, Future of Software Engineering (FOSE '07).

[19]  Bengt Jonsson,et al.  A logic for reasoning about time and reliability , 1990, Formal Aspects of Computing.

[20]  Timothy A. Davis,et al.  Direct methods for sparse linear systems , 2006, Fundamentals of algorithms.

[21]  Katerina Goseva-Popstojanova,et al.  Architecture-based approach to reliability assessment of software systems , 2001, Perform. Evaluation.

[22]  Sheldon M. Ross,et al.  Stochastic Processes , 2018, Gauge Integral Structures for Stochastic Calculus and Quantum Electrodynamics.

[23]  Conrado Daws Symbolic and Parametric Model Checking of Discrete-Time Markov Chains , 2004, ICTAC.

[24]  Alan Bundy,et al.  Constructing Induction Rules for Deductive Synthesis Proofs , 2006, CLASE.

[25]  Charles M. Grinstead,et al.  Introduction to probability , 1999, Statistics for the Behavioural Sciences.

[26]  Zhiming Liu,et al.  Theoretical Aspects of Computing - ICTAC 2004, First International Colloquium, Guiyang, China, September 20-24, 2004, Revised Selected Papers , 2005, ICTAC.

[27]  Ulrich Herzog,et al.  Formal Methods for Performance Evaluation , 2002, European Educational Forum: School on Formal Methods and Performance Analysis.

[28]  Christel Baier,et al.  Model-Checking Algorithms for Continuous-Time Markov Chains , 2002, IEEE Trans. Software Eng..

[29]  John N. Tsitsiklis,et al.  Introduction to Probability , 2002 .

[30]  Mihalis Yannakakis,et al.  The complexity of probabilistic verification , 1995, JACM.

[31]  Adnan Aziz,et al.  It Usually Works: The Temporal Logic of Stochastic Systems , 1995, CAV.

[32]  Joost-Pieter Katoen,et al.  How Fast and Fat Is Your Probabilistic Model Checker? An Experimental Performance Comparison , 2007, Haifa Verification Conference.

[33]  Carlo Ghezzi,et al.  Architectural Issues of Adaptive Pervasive Systems , 2010, Graph Transformations and Model-Driven Engineering.

[34]  Hongyang Qu,et al.  Incremental quantitative verification for Markov decision processes , 2011, 2011 IEEE/IFIP 41st International Conference on Dependable Systems & Networks (DSN).

[35]  Radu Calinescu,et al.  Dynamic QoS Management and Optimization in Service-Based Systems , 2011, IEEE Transactions on Software Engineering.

[36]  Luciano Baresi,et al.  Toward Open-World Software: Issue and Challenges , 2006, Computer.

[37]  Roger C. Cheung,et al.  A User-Oriented Software Reliability Model , 1978, IEEE Transactions on Software Engineering.

[38]  Carlo Ghezzi,et al.  Run-time efficient probabilistic model checking , 2011, 2011 33rd International Conference on Software Engineering (ICSE).

[39]  Holger Hermanns,et al.  Discrete-time rewards model-checked (to appear) , 2003 .

[40]  Sheldon M. Ross Introduction to Probability Models. , 1995 .