Static Driver Verifier Research Articles

Distributed bounded model checking

Program verification is a resource-hungry task. This paper looks at the problem of parallelizing SMT-based automated program verification, specifically bounded model-checking, so that it can be distributed and executed on a cluster of machines. We present an algorithm that dynamically unfolds the call graph of the program and frequently splits it to create sub-tasks that can be solved in parallel. The algorithm is adaptive, controlling the splitting rate according to available resources, and also leverages information from the SMT solver to split where most complexity lies in the search. We implemented our algorithm by modifying Corral, the verifier used by Microsoft’s Static Driver Verifier (SDV), and evaluate it on a series of hard SDV benchmarks.

Static Analysis Using the Cloud

In this paper we describe our experience of using Microsoft Azure cloud computing platform for static analysis. We start by extending Static Driver Verifier to operate in the Microsoft Azure cloud with significant improvements in performance and scalability. We present our results of using SDV on single drivers and driver suites using various configurations of the cloud relative to a local machine. Finally, we describe the Static Module Verifier platform, a highly extensible and configurable platform for static analysis of generic modules, where we have integrated support for verification using a cloud services provider (Microsoft Azure in this case).

Proofs from Tests

We present an algorithm DASH to check if a program P satisfies a safety property φ. The unique feature of this algorithm is that it uses only test generation operations, and it refines and maintains a sound program abstraction as a consequence of failed test generation operations. Thus, each iteration of the algorithm is inexpensive, and can be implemented without any global may-alias information. In particular, we introduce a new refinement operator WPα that uses only the alias information obtained by symbolically executing a test to refine abstractions in a sound manner. We present a full exposition of the DASH algorithm and its theoretical properties. We have implemented DASH in a tool called YOGI that plugs into Microsoft's Static Driver Verifier framework. We have used this framework to run YOGI on 69 Windows Vista drivers with 85 properties and find that YOGI scales much better than SLAM, the current engine driving Microsoft's Static Driver Verifier.

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.