Real-Time Model Checking on Secondary Storage

Edelkamp, Stefan; Jabbar, Shahid

doi:10.1007/978-3-540-74128-2_5

Cited by 4 publications

(3 citation statements)

References 20 publications

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“…In this context, Edelkamp and Jabbar [11] have proposed three external search algorithms for priced timed automata, which they have implemented in the Uppaal Cora model checker.…”

Section: Related Workmentioning

confidence: 99%

Downward pattern refinement for timed automata

Wehrle

Kupferschmid²

2014

Int J Softw Tools Technol Transfer

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Directed model checking is a well-established approach for detecting error states in concurrent systems. A popular variant to find shortest error traces is to apply the A * search algorithm with distance heuristics that never overestimate the real error distance. An important class of such distance heuristics is the class of pattern database heuristics. Pattern database heuristics are built on abstractions of the system under consideration. In this paper, we propose downward pattern refinement, a systematic approach for the construction of pattern database heuristics for concurrent systems of timed automata. First, we propose a general framework for pattern databases in the context of timed automata and show that desirable theoretical properties hold for the resulting pattern database. Afterward, we formally define a concept to measure the accuracy of abstractions. Based on this concept, we propose an algorithm for computing succinct abstractions that are still accurate to produce informed pattern databases. We evaluate our approach on large and complex industrial problems. The experiments show the practical potential of the resulting pattern database heuristic.

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“…In this context, Edelkamp and Jabbar [11] have proposed three external search algorithms for priced timed automata, which they have implemented in the Uppaal Cora model checker.…”

Section: Related Workmentioning

confidence: 99%

Downward pattern refinement for timed automata

Wehrle

Kupferschmid²

2014

Int J Softw Tools Technol Transfer

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“…In order to ensure the accurate and precise modeling [1][2][3][4] of complex real-world situations, the utilization of robust model structures such as Kripke structures 5,6 , multi-valued Kripke structures 7,8 , fuzzy Kripke structures 9,10 , and possibility Kripke structures [11][12][13][14] is essential. These model structures find applicability across a spectrum of complex domains, encompassing strategic decision-making, cancer metabolism investigation, security protocol verification, artificial intelligence analysis, robotics advancements, control systems optimization, and various other fields [15][16][17][18] . Particularly salient is the role of nondeterministic fuzzy Kripke structures in addressing the complexities inherent to real-world situations marked by uncertainty 19,20 .…”

Section: Introductionmentioning

confidence: 99%

Theoretical foundations, computational methods, and practical applications of nondeterministic fuzzy abstraction

Deng

2023

Preprint

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Efficient and effective modeling of complex real-world situations characterized by uncertainty poses a substantial challenge. This study addresses this challenge by introducing a nondeterministic fuzzy abstraction approach to enhance efficiency and effectiveness in this context. The theoretical framework establishes a standardized representation of nondeterministic fuzzy Kripke structures, accompanied by definitions associated with nondeterministic fuzzy abstraction. The methods section presents an algorithm for realizing nondeterministic fuzzy abstraction, and it is well-suited for practical implementation in computer programs. The results section demonstrates the practical application of the proposed approach by modeling the process of treating a patient with ulcers in the terminal ileum. In the discussion section, a comparative study of nondeterministic fuzzy abstraction underscores its broad applicability and compatibility across various uncertainty and certainty model structures.

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“…Over the years, many techniques have been introduced to fight it, e.g. partial order reduction [16,5], using secondary storage [8,17], distributed model checking [13,3,2], and directed model checking [9,10].…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Adaptive State Space Caching Based on Level Sampling

Mateescu

Wijs

2009

Tools and Algorithms for the Construction and Analysis of Systems

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Abstract. In the past, several attempts have been made to deal with the state space explosion problem by equipping a depth-first search (DFS) algorithm with a state cache, or by avoiding collision detection, thereby keeping the state hash table at a fixed size. Most of these attempts are tailored specifically for DFS, and are often not guaranteed to terminate and/or to exhaustively visit all the states. In this paper, we propose a general framework of hierarchical caches which can also be used by breadth-first searches (BFS). Our method, based on an adequate sampling of BFS levels during the traversal, guarantees that the BFS terminates and traverses all transitions of the state space. We define several (static or adaptive) configurations of hierarchical caches and we study experimentally their effectiveness on benchmark examples of state spaces and on several communication protocols, using a generic implementation of the cache framework that we developed within the CADP toolbox.

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Real-Time Model Checking on Secondary Storage

Cited by 4 publications

References 20 publications

Downward pattern refinement for timed automata

Downward pattern refinement for timed automata

Theoretical foundations, computational methods, and practical applications of nondeterministic fuzzy abstraction

Hierarchical Adaptive State Space Caching Based on Level Sampling

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