PDF
] Keyword(s): Software Model Checking.
WWW
] [ PDF
] Keyword(s): Software Model Checking.
PDF
] Keyword(s): Software Model Checking.
PDF
] Keyword(s): Software Model Checking.
PDF
] Keyword(s): Software Model Checking.
WWW
] [ PDF
] Keyword(s): Software Model Checking.
| Verification is a complex algorithmic task, requiring large amounts of computing resources. One approach to reduce the resource consumption is to reuse information from previous verification runs. This paper gives an overview of three techniques for such information reuse. Conditional model checking outputs a condition that describes the state space that was successfully verified, and accepts as input a condition that instructs the model checker which parts of the system should be verified; thus, later verification runs can use the output condition of previous runs in order to not verify again parts of the state space that were already verified. Precision reuse is a technique to use intermediate results from previous verification runs to accelerate further verification runs of the system; information about the level of abstraction in the abstract model can be reused in later verification runs. Typical model checkers provide an error path through the system as witness for having proved that a system violates a property, and a few model checkers provide some kind of proof certificate as a witness for the correctness of the system; these witnesses should be such that the verifiers can read them and ---with less computational effort--- (re-) verify that the witness is valid. |
PDF
] Keyword(s): Software Model Checking.
| The success of software model checking depends on finding an appropriate abstraction of the subject program. The choice of the abstract domain and the analysis configuration is currently left to the user, who may not be familiar with the tradeoffs and performance details of the available abstract domains. We introduce the concept of domain types, which classify the program variables into types that are more fine-grained than standard declared types, such as int or long, in order to guide the selection of an appropriate abstract domain for a model checker. Our implementation determines the domain type for each variable in a pre-processing step, based on the variable usage in the program, and then assigns each variable to an abstract domain. The model-checking framework that we use supports to specify a separate analysis precision for each abstract domain, such that we can freely configure the analysis. We experimentally demonstrate a significant impact of the choice of the abstract domain per variable. We consider one explicit (hash tables for integer values) and one symbolic (binary decision diagrams) domain. The experiments are based on standard verification tasks that are taken from recent competitions on software verification. Each abstract domain has unique advantages in representing the state space of variables of a certain domain type. Our experiments show that software model checkers can be improved with a domain-type guided combination of abstract domains. |
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Online: http://arxiv.org/abs/1305.6640 |
PDF
] Keyword(s): Software Model Checking.
| Continuous testing during development is a well-established technique for software-quality assurance. Continuous model checking from revision to revision is not yet established as a standard practice, because the enormous resource consumption makes its application impractical. Model checkers compute a large number of verification facts that are necessary for verifying if a given specification holds. We have identified a category of such intermediate results that are easy to store and efficient to reuse: abstraction precisions. The precision of an abstract domain specifies the level of abstraction that the analysis works on. Precisions are thus a precious result of the verification effort and it is a waste of resources to throw them away after each verification run. In particular, precisions are small and thus easy to store; they are easy to process and have a large impact on resource consumption. We experimentally show the impact of precision reuse on industrial verification problems, namely, 59 device drivers with 1119 revisions from the Linux kernel. |
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Online: http://arxiv.org/abs/1305.6915 |
Dissertation
PS