Proving Memory Safety of the ANI Windows Image Parser Using Compositional Exhaustive Testing

We report in this paper how we proved memory safety of a complex Windows image parser written in low-level C in only three months of work and using only three core tech-niques, namely (1) symbolic execution at the x86 binary level, (2) exhaustive program path enumeration and testing, and (3) user-guided program decomposition and summariza-tion. We also used a new tool, named MicroX, for executing code fragments in isolation using a custom virtual machine designed for testing purposes. As a result of this work, we are able to prove, for the first time, that a Windows image parser is memory safe, i.e., free of any buffer-overflow secu-rity vulnerabilities, modulo the soundness of our tools and several additional assumptions regarding bounding input-dependent loops, fixing a few buffer-overflow bugs, and ex-cluding some code parts that are not memory safe by design. In the process, we also discovered and fixed several limita-tions in our tools, and narrowed the gap between systematic testing and verification. 1

Danger Invariants

Static analysers search for overapproximating proofs of safety commonly known as safety invariants. Conversely, static bug finders (e.g. Bounded Model Checking) give evidence for the failure of an assertion in the form of a counterexample trace. As opposed to safety invariants, the size of a counterexample is dependent on the depth of the bug, i.e., the length of the execution trace prior to the error state, which also determines the computational effort required to find them. We propose a way of expressing danger proofs that is independent of the depth of bugs. Essentially, such danger proofs constitute a compact representation of a counterexample trace, which we call a danger invariant. Danger invariants summarise sets of traces that are guaranteed to be able to reach an error state. Our conjecture is that such danger proofs will enable the design of bug finding analyses for which the computational effort is independent of the depth of bugs, and thus find deep bugs more efficiently. As an exemplar of an analysis that uses danger invariants, we design a bug finding technique based on a synthesis engine. We implemented this technique and compute danger invariants for intricate programs taken from SV-COMP 2016

Program Synthesis for Program Analysis

In this article, we propose a unified framework for designing static analysers based on program synthesis. For this purpose, we identify a fragment of second-order logic with restricted quantification that is expressive enough to model numerous static analysis problems (e.g., safety proving, bug finding, termination and non-termination proving, refactoring). As our focus is on programs that use bit-vectors, we build a decision procedure for this fragment over finite domains in the form of a program synthesiser. We provide instantiations of our framework for solving a diverse range of program verification tasks such as termination, non-termination, safety and bug finding, superoptimisation, and refactoring. Our experimental results show that our program synthesiser compares positively with specialised tools in each area as well as with general-purpose synthesisers

Combining Model Checking and Testing

Abstract Model checking and testing have a lot in common. Over the last two decades, significant progress has been made on how to broaden the scope of model checking from finite-state abstractions to actual software implementations. One way to do this consists of adapting model checking into a form of systematic testing that is applicable to industrial-size software. This chapter presents an overview of this strand of software model checking

Program Synthesis for Program Analysis

In this paper, we propose a unified framework for designing static analysers based on program synthesis. For this purpose, we identify a fragment of second-order logic with restricted quantification that is expressive enough to model numerous static analysis problems (e.g., safety proving, bug finding, termination and non-termination proving, refactoring). As our focus is on programs that use bit-vectors, we build a decision procedure for this fragment over finite domains in the form of a program synthesiser. We provide instantiations of our framework for solving a diverse range of program verification tasks such as termination, non-termination, safety and bug finding, superoptimisation and refactoring. Our experimental results show that our program synthesiser compares positively with specialised tools in each area as well as with general-purpose synthesisers

Exploiting Similarity Patterns to Build Generic Test Case Templates for Software Product Line Testing

Ph.DDOCTOR OF PHILOSOPH

An Empirical Study of Optimizations in YOGI

Though verification tools are finding industrial use, the utility of engineering optimizations that make them scalable and usable is not widely known. Despite the fact that several optimizations are part of folklore in the communities that develop these tools, no rigorous evaluation of these optimizations has been done before. We describe and evaluate several engineering optimizations implemented in the Yogi property checking tool, including techniques to pick an initial abstraction, heuristics to pick predicates for refinement, optimizations for interprocedural analysis, and optimizations for testing. We believe that our empirical evaluation gives the verification community useful information about which optimizations they could implement in their tools, and what gains they can realistically expect from these optimizations