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Greedy Best-First Search: It uses a heuristic that attempts to predict how close the end of a path is to the goal state, so that paths which are judged to be closer to the state are explored first.
Best-First Search: Best-first search is a variant of BFS which improves it by exploring the most promising neighboring state chosen according to the result of the evaluation function for that state. E
What are the various factors on which heuristic evaluation function depends?
Heuristic Search: Heuristic search exploits the information known about the system under analysis in order to accelerate the search process. The basic idea of heuristic search is that rather than tryi
Bounded Searches: Bounded Depth-First Search (BDFS) works exactly like Depth-First Search, however avoids some of its drawbacks by imposing a maximum limit on the depth of the search. Even when the s
DFS and BFS Tradeoffs: In general, DFS will have lower memory (space) complexity than BFS since only the information about the states and transitions on the current path needs to be stored. This contr
Breadth-First Search: With Breadth-First Search (BFS), the model checker again begins at the start state and explores all its outgoing transitions to the neighboring states. Then for each of those nea
Depth-First Search: With Depth-First Search (DFS), the model checker begins at the start state and explores each of its outgoing transitions to other states as far as possible along each branch before
Search Strategies in model checking: Model checkers such as JPF and SPIN support a number of search strategies used to explore the state space of the program. Two of these strategies are the most well
Search and Partial Coverage: A model checker can be used to verify the correctness of a finite-state system with respect to a desired property by searching a labeled state-transition graph that models
Promela primitives implementing C code: Several Promela primitives can be used to connect a verification model to implementation-level C code: c_decl introduces the types and names of externally dec
Model-driven verification, as implemented in the SPIN tool, advocates the use of abstraction mappings during concrete model checking to efficiently analyze an under-approximation of the feasible behav
Predicate Abstraction: Predicate abstraction (Graf and Saïdi 1997), is a special case of an over-approximating data abstraction which maps a potentially infinite-state program into a finite-state
Heap Abstractions: The class abstractions that we discussed above are obtained by abstracting each field of base type. The number of instances of that particular class still needs to be bounded; this
The abstract list domain is as shown: {empty, some, [d1], [d2],[ d1, d2], [d2, d1], all}. A) empty represents empty lists. B) some represents lists with only other values. C) [d1] represents lis
Container Abstractions: Abstractions for containers (such as lists, stacks, sets, or queues) may represent just the state of a container—e.g., full or empty—and abstract away from the actu
Data Independence: A system is data independent if the values of the infinite program data it manipulates are not relevant to the behavior of the system—for example, a protocol that transmits me
Abstractions for Data Structures: Abstraction of data structures such as Java classes can be achieved by component-wise abstraction of each field in a class. This approach is taken by Bandera (Dwyer e
Useful Abstractions: We discuss here several useful data abstractions for integer domains. Abstractions for other numeric domains can be defined similarly. A) A Range abstraction tracks concrete valu
Abstract Interpretation: We present an abstract interpretation in an informal manner, as a collection of three components: 1. A finite domain of abstract values; 2. An abstraction function that maps
Data Abstraction: Our presentation for data abstraction follows the abstract interpretation framework (Cousot and Cousot 1999). Abstract interpretation establishes a rigorous methodology for building
Exact Abstraction: An abstraction is exact (with respect to a property or a class of properties) if there is no loss of information due to abstraction. Exact abstractions are strongly property preserv
Under-approximation: Under-approximation occurs when behaviors are removed to create the abstract system. This approach corresponds to error-preserving abstractions (where the properties are safety or
Over-approximation: Over-approximation occurs when there are more behaviors in the abstract system than the concrete system. This approach provides a popular class of weakly preserving abstractions fo
Behavior Approximation: Abstractions are also described in term of the relative behavior of the abstract system versus the concrete system. A program’s “behavior” is defined as the s