Optimal and Perfectly Parallel Algorithms for On-demand Data-Flow Analysis

Interprocedural data-flow analyses form an expressive and useful paradigm of numerous static analysis applications, such as live variables analysis, alias analysis and null pointers analysis. The most widely-used framework for interprocedural data-flow analysis is IFDS, which encompasses distributive data-flow functions over a finite domain. On-demand data-flow analyses restrict the focus of the analysis on specific program locations and data facts. This setting provides a natural split between (i) an offline (or preprocessing) phase, where the program is partially analyzed and analysis summaries are created, and (ii) an online (or query) phase, where analysis queries arrive on demand and the summaries are used to speed up answering queries.In this work, we consider on-demand IFDS analyses where the queries concern program locations of the same procedure (aka same-context queries). We exploit the fact that flow graphs of programs have low treewidth to develop faster algorithms that are space and time optimal for many common data-flow analyses, in both the preprocessing and the query phase. We also use treewidth to develop query solutions that are embarrassingly parallelizable, i.e. the total work for answering each query is split to a number of threads such that each thread performs only a constant amount of work. Finally, we implement a static analyzer based on our algorithms, and perform a series of on-demand analysis experiments on standard benchmarks. Our experimental results show a drastic speed-up of the queries after only a lightweight preprocessing phase, which significantly outperforms existing techniques.

Semantic Foundations for Deterministic Dataflow and Stream Processing

We propose a denotational semantic framework for deterministic dataflow and stream processing that encompasses a variety of existing streaming models. Our proposal is based on the idea that data streams, stream transformations, and stream-processing programs should be classified using types. The type of a data stream is captured formally by a monoid, an algebraic structure with a distinguished binary operation and a unit. The elements of a monoid model the finite fragments of a stream, the binary operation represents the concatenation of stream fragments, and the unit is the empty fragment. Stream transformations are modeled using monotone functions on streams, which we call stream transductions. These functions can be implemented using abstract machines with a potentially infinite state space, which we call stream transducers. This abstract typed framework of stream transductions and transducers can be used to (1) verify the correctness of streaming computations, that is, that an implementation adheres to the desired behavior, (2) prove the soundness of optimizing transformations, e.g. for parallelization and distribution, and (3) inform the design of programming models and query languages for stream processing. In particular, we show that several useful combinators can be supported by the full class of stream transductions and transducers: serial composition, parallel composition, and feedback composition.

Concise Read-Only Specifications for Better Synthesis of Programs with Pointers

In program synthesis there is a well-known trade-off between concise and strong specifications: if a specification is too verbose, it might be harder to write than the program; if it is too weak, the synthesised program might not match the user’s intent. In this work we explore the use of annotations for restricting memory access permissions in program synthesis, and show that they can make specifications much stronger while remaining surprisingly concise. Specifically, we enhance Synthetic Separation Logic (SSL), a framework for synthesis of heap-manipulating programs, with the logical mechanism of read-only borrows.We observe that this minimalistic and conservative SSL extension benefits the synthesis in several ways, making it more (a) expressive (stronger correctness guarantees are achieved with a modest annotation overhead), (b) effective (it produces more concise and easier-to-read programs), (c) efficient (faster synthesis), and (d) robust (synthesis efficiency is less affected by the choice of the search heuristic). We explain the intuition and provide formal treatment for read-only borrows. We substantiate the claims (a)–(d) by describing our quantitative evaluation of the borrowing-aware synthesis implementation on a series of standard benchmark specifications for various heap-manipulating programs.

Higher-Ranked Annotation Polymorphic Dependency Analysis

The precision of a static analysis can be improved by increasing the context-sensitivity of the analysis. In a type-based formulation of static analysis for functional languages this can be achieved by, e.g., introducing let-polyvariance or subtyping. In this paper we go one step further by defining a higher-ranked polyvariant type system so that even properties of lambda-bound identifiers can be generalized over. We do this for dependency analysis, a generic analysis that can be instantiated to a range of different analyses that in this way all can profit.We prove that our analysis is sound with respect to a call-by-name semantics and that it satisfies a so-called noninterference property. We provide a type reconstruction algorithm that we have proven to be terminating, and sound and complete with respect to its declarative specification. Our principled description can serve as a blueprint for making other analyses higher-ranked.

RustHorn: CHC-Based Verification for Rust Programs

Reduction to the satisfiablility problem for constrained Horn clauses (CHCs) is a widely studied approach to automated program verification. The current CHC-based methods for pointer-manipulating programs, however, are not very scalable. This paper proposes a novel translation of pointer-manipulating Rust programs into CHCs, which clears away pointers and heaps by leveraging ownership. We formalize the translation for a simplified core of Rust and prove its correctness. We have implemented a prototype verifier for a subset of Rust and confirmed the effectiveness of our method.

Trace-Relating Compiler Correctness and Secure Compilation

Compiler correctness is, in its simplest form, defined as the inclusion of the set of traces of the compiled program into the set of traces of the original program, which is equivalent to the preservation of all trace properties. Here traces collect, for instance, the externally observable events of each execution. This definition requires, however, the set of traces of the source and target languages to be exactly the same, which is not the case when the languages are far apart or when observations are fine-grained. To overcome this issue, we study a generalized compiler correctness definition, which uses source and target traces drawn from potentially different sets and connected by an arbitrary relation. We set out to understand what guarantees this generalized compiler correctness definition gives us when instantiated with a non-trivial relation on traces. When this trace relation is not equality, it is no longer possible to preserve the trace properties of the source program unchanged. Instead, we provide a generic characterization of the target trace property ensured by correctly compiling a program that satisfies a given source property, and dually, of the source trace property one is required to show in order to obtain a certain target property for the compiled code. We show that this view on compiler correctness can naturally account for undefined behavior, resource exhaustion, different source and target values, side-channels, and various abstraction mismatches. Finally, we show that the same generalization also applies to many secure compilation definitions, which characterize the protection of a compiled program against linked adversarial code.

Connecting Higher-Order Separation Logic to a First-Order Outside World

Separation logic is a useful tool for proving the correctness of programs that manipulate memory, especially when the model of memory includes higher-order state: Step-indexing, predicates in the heap, and higher-order ghost state have been used to reason about function pointers, data structure invariants, and complex concurrency patterns. On the other hand, the behavior of system features (e.g., operating systems) and the external world (e.g., communication between components) is usually specified using first-order formalisms. In principle, the soundness theorem of a separation logic is its interface with first-order theorems, but the soundness theorem may implicitly make assumptions about how other components are specified, limiting its use. In this paper, we show how to extend the higher-order separation logic of the Verified Software Toolchain to interface with a first-order verified operating system, in this case CertiKOS, that mediates its interaction with the outside world. The resulting system allows us to prove the correctness of C programs in separation logic based on the semantics of system calls implemented in CertiKOS. It also demonstrates that the combination of interaction trees + CompCert memories serves well as a lingua franca to interface and compose two quite different styles of program verification.

Solving Program Sketches with Large Integer Values

Program sketching is a program synthesis paradigm in which the programmer provides a partial program with holes and assertions. The goal of the synthesizer is to automatically find integer values for the holes so that the resulting program satisfies the assertions. The most popular sketching tool, Sketch, can efficiently solve complex program sketches, but uses an integer encoding that often performs poorly if the sketched program manipulates large integer values. In this paper, we propose a new solving technique that allows Sketch to handle large integer values while retaining its integer encoding. Our technique uses a result from number theory, the Chinese Remainder Theorem, to rewrite program sketches to only track the remainders of certain variable values with respect to several prime numbers. We prove that our transformation is sound and the encoding of the resulting programs are exponentially more succinct than existing Sketch encodings. We evaluate our technique on a variety of benchmarks manipulating large integer values. Our technique provides speedups against both existing Sketch solvers and can solve benchmarks that existing Sketch solvers cannot handle.

ARMv8-A System Semantics: Instruction Fetch in Relaxed Architectures

Computing relies on architecture specifications to decouple hardware and software development. Historically these have been prose documents, with all the problems that entails, but research over the last ten years has developed rigorous and executable-as-test-oracle specifications of mainstream architecture instruction sets and “user-mode” concurrency, clarifying architectures and bringing them into the scope of programming-language semantics and verification. However, the system semantics, of instruction-fetch and cache maintenance, exceptions and interrupts, and address translation, remains obscure, leaving us without a solid foundation for verification of security-critical systems software.In this paper we establish a robust model for one aspect of system semantics: instruction fetch and cache maintenance for ARMv8-A. Systems code relies on executing instructions that were written by data writes, e.g. in program loading, dynamic linking, JIT compilation, debugging, and OS configuration, but hardware implementations are often highly optimised, e.g. with instruction caches, linefill buffers, out-of-order fetching, branch prediction, and instruction prefetching, which can affect programmer-observable behaviour. It is essential, both for programming and verification, to abstract from such microarchitectural details as much as possible, but no more. We explore the key architecture design questions with a series of examples, discussed in detail with senior Arm staff; capture the architectural intent in operational and axiomatic semantic models, extending previous work on “user-mode” concurrency; make these models executable as test oracles for small examples; and experimentally validate them against hardware behaviour (finding a bug in one hardware device). We thereby bring these subtle issues into the mathematical domain, clarifying the architecture and enabling future work on system software verification.

Modular Inference of Linear Types for Multiplicity-Annotated Arrows

Bernardy et al. [2018] proposed a linear type system $$\lambda ^q_\rightarrow $$ λ → q as a core type system of Linear Haskell. In the system, linearity is represented by annotated arrow types $$A \rightarrow _m B$$ A → m B , where m denotes the multiplicity of the argument. Thanks to this representation, existing non-linear code typechecks as it is, and newly written linear code can be used with existing non-linear code in many cases. However, little is known about the type inference of $$\lambda ^q_\rightarrow $$ λ → q . Although the Linear Haskell implementation is equipped with type inference, its algorithm has not been formalized, and the implementation often fails to infer principal types, especially for higher-order functions. In this paper, based on OutsideIn(X) [Vytiniotis et al., 2011], we propose an inference system for a rank 1 qualified-typed variant of $$\lambda ^q_\rightarrow $$ λ → q , which infers principal types. A technical challenge in this new setting is to deal with ambiguous types inferred by naive qualified typing. We address this ambiguity issue through quantifier elimination and demonstrate the effectiveness of the approach with examples.