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Selfadaptive software needs quantitative verification at runtime
 Communications of the ACM
"... Software is surreptitiously becoming the backbone of modern society. Most human activities are either software enabled or entirely managed by software. Examples range from healthcare and transportation to commerce and manufacturing. In all these applications, one requirement is becoming common: soft ..."
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Software is surreptitiously becoming the backbone of modern society. Most human activities are either software enabled or entirely managed by software. Examples range from healthcare and transportation to commerce and manufacturing. In all these applications, one requirement is becoming common: software must adapt continuously, to respond to changes in application
Partial order reduction for model checking Markov decision processes under unconditional
, 2012
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An incremental verification framework for componentbased software systems
 in CBSE’13, 2013
"... We present a toolsupported framework for the efficient reverification of componentbased software systems after changes such as additions, removals or modifications of components. The incremental verification engine at the core of our INcremental VErification STrategy (INVEST) framework uses high ..."
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We present a toolsupported framework for the efficient reverification of componentbased software systems after changes such as additions, removals or modifications of components. The incremental verification engine at the core of our INcremental VErification STrategy (INVEST) framework uses highlevel algebraic representations of componentbased systems to identify and execute the minimal set of componentwise reverification steps after a system change. The generality of the INVEST engine allows its integration with existing assumeguarantee verification paradigms. We illustrate this integration for an existing technique for the assumeguarantee verification of probabilistic systems. The resulting instance of the INVEST framework can reverify probabilistic safety properties of a clouddeployed software system in a fraction of the time required by compositional assumeguarantee verification alone.
Incremental synthesis of control policies for heterogeneous multiagent systems with linear temporal logic specification
 In ICRA
, 2013
"... Abstract — We consider automatic synthesis of control policies for nonindependent, heterogeneous multiagent systems with the objective of maximizing the probability of satisfying a given specification. The specification is expressed as a formula in linear temporal logic. The agents are modeled by ..."
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Abstract — We consider automatic synthesis of control policies for nonindependent, heterogeneous multiagent systems with the objective of maximizing the probability of satisfying a given specification. The specification is expressed as a formula in linear temporal logic. The agents are modeled by Markov decision processes with a common set of actions. These actions, however, may or may not affect the behaviors of all the agents. To alleviate the wellknown state explosion problem, an incremental approach is proposed where only a small subset of agents is incorporated in the synthesis procedure initially and more agents are successively added until the limitations on computational resources are reached. The proposed algorithm runs in an anytime fashion, where the probability of satisfying the specification increases as the algorithm progresses. I.
Improving gpu sparse matrixvector multiplication for probabilistic model checking,” Model Checking Software
, 2012
"... Abstract. We present several methods to improve the run times of probabilistic model checking on generalpurpose graphics processing units (GPUs). The methods enhance sparse matrixvector multiplications, which are in the core of the probabilistic model checking algorithms. The improvement is based ..."
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Abstract. We present several methods to improve the run times of probabilistic model checking on generalpurpose graphics processing units (GPUs). The methods enhance sparse matrixvector multiplications, which are in the core of the probabilistic model checking algorithms. The improvement is based on the analysis of the transition matrix structures corresponding to state spaces of different examples from the literature. Our first method defines an enumeration of the matrix elements (states of the Markov chains), based on breadthfirst search which can lead to a more regular representation of the matrices. We introduce two additional methods that adjust the execution paths and memory access patterns of the individual processors of the GPU. They exploit the specific features of the transition matrices arising from probabilistic/stochastic models as well as the logical and physical architectures of the device. We implemented the matrix reindexing and the efficient memory access methods in GPUPRISM, an extension of the probabilistic model checker PRISM. The experiments with the prototype implementation show that each of the methods can bring a significant run time improvementmore than four times compared to the previous version of GPUPRISM. Moreover, in some cases, the methods are orthogonal and can be used in combination to achieve even greater speed ups. 1
Incremental temporal logic synthesis of control policies for robots interacting with dynamic agents
 In Proc. 25th International Conference on Intelligent Robots and Systems (IROS’12
, 2012
"... Abstract — We consider the synthesis of control policies from temporal logic specifications for robots that interact with multiple dynamic environment agents. Each environment agent is modeled by a Markov chain whereas the robot is modeled by a finite transition system (in the deterministic case) or ..."
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Abstract — We consider the synthesis of control policies from temporal logic specifications for robots that interact with multiple dynamic environment agents. Each environment agent is modeled by a Markov chain whereas the robot is modeled by a finite transition system (in the deterministic case) or Markov decision process (in the stochastic case). Existing results in probabilistic verification are adapted to solve the synthesis problem. To partially address the state explosion issue, we propose an incremental approach where only a small subset of environment agents is incorporated in the synthesis procedure initially and more agents are successively added until we hit the constraints on computational resources. Our algorithm runs in an anytime fashion where the probability that the robot satisfies its specification increases as the algorithm progresses. I.
Probabilistic verification at runtime for selfadaptive systems
 Institute for Software Technology (ISTE) University of Stuttgart
, 2013
"... Abstract. An effective design of effective and efficient selfadaptive 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 poten ..."
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Abstract. An effective design of effective and efficient selfadaptive 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 onthefly analysis, in terms of execution time and memory occupation. For this reason, efficient runtime model checking represents a crucial research challenge. This paper precisely addresses this issue and focuses on probabilistic runtime model checking in which reliability models are given in terms of Discrete Time Markov Chains which are verified at runtime against a set of requirements expressed as logical formulae. In particular, the paper discusses the use of probabilistic model checking at runtime for selfadaptive systems by surveying and comparing the existing approaches divided in two categories: stateelimination algorithms and algebrabased algorithms. The discussion is supported by a realistic example and by empirical experiments.
On Incremental Quantitative Verification for Probabilistic Systems
"... Quantitative verification techniques offer an effective means of computing performance and reliability properties for a wide range of systems. In many cases, it is necessary to perform repeated analyses of a system, for example to identify trends in results, determine optimal system parameters or wh ..."
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Quantitative verification techniques offer an effective means of computing performance and reliability properties for a wide range of systems. In many cases, it is necessary to perform repeated analyses of a system, for example to identify trends in results, determine optimal system parameters or when performing online analysis for adaptive systems. We argue the need for incremental quantitative verification techniques which are able to reuse results from previous verification runs in order to improve efficiency. We report on recently proposed techniques for incremental quantitative verification of Markov decision processes, based on a decomposition of the model into its strongly connected components. We give an overview of the method, describe a number of useful optimisations and show experimental results that illustrate significant gains in runtime performance using the incremental approach. 1
Incremental Runtime Verification of Probabilistic Systems
"... Abstract. Probabilistic verification techniques have been proposed for runtime analysis of adaptive software systems, with the verification results being used to steer the system so that it satisfies certain QualityofService requirements. Since systems evolve over time, and verification results are ..."
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Abstract. Probabilistic verification techniques have been proposed for runtime analysis of adaptive software systems, with the verification results being used to steer the system so that it satisfies certain QualityofService requirements. Since systems evolve over time, and verification results are required promptly, efficiency is an essential issue. To address this, we present incremental verification techniques, which exploit the results of previous analyses. We target systems modelled as Markov decision processes, developing incremental methods for constructing models from highlevel system descriptions and for numerical solution using policy iteration based on strongly connected components. A prototype implementation, based on the PRISM model checker, demonstrates performance improvements on a range of case studies. 1