8 research outputs found
Enhancing Reuse of Constraint Solutions to Improve Symbolic Execution
Constraint solution reuse is an effective approach to save the time of
constraint solving in symbolic execution. Most of the existing reuse approaches
are based on syntactic or semantic equivalence of constraints; e.g. the Green
framework is able to reuse constraints which have different representations but
are semantically equivalent, through canonizing constraints into syntactically
equivalent normal forms. However, syntactic/semantic equivalence is not a
necessary condition for reuse--some constraints are not syntactically or
semantically equivalent, but their solutions still have potential for reuse.
Existing approaches are unable to recognize and reuse such constraints.
In this paper, we present GreenTrie, an extension to the Green framework,
which supports constraint reuse based on the logical implication relations
among constraints. GreenTrie provides a component, called L-Trie, which stores
constraints and solutions into tries, indexed by an implication partial order
graph of constraints. L-Trie is able to carry out logical reduction and logical
subset and superset querying for given constraints, to check for reuse of
previously solved constraints. We report the results of an experimental
assessment of GreenTrie against the original Green framework, which shows that
our extension achieves better reuse of constraint solving result and saves
significant symbolic execution time.Comment: this paper has been submitted to conference ISSTA 201
Thorough Static Analysis of Device Drivers
Bugs in kernel-level device drivers cause 85% of the system crashes in the Windows XP operating system [44]. One of the sources of these errors is the complexity of the Windows driver API itself: programmers must master a complex set of rules about how to use the driver API in order to create drivers that are good clients of the kernel. We have built a static analysis engine that finds API usage errors in C programs. The Static Driver Verifier tool (SDV) uses this engine to find kernel API usage errors in a driver. SDV includes models of the OS and the environment of the device driver, and over sixty API usage rules. SDV is intended to be used by driver developers "out of the box." Thus, it has stringent requirements: (1) complete automation with no input from the user; (2) a low rate of false errors. We discuss the techniques used in SDV to meet these requirements, and empirical results from running SDV on over one hundred Windows device drivers