Modular, higher order cardinality analysis in theory and practice

Since the mid '80s, compiler writers for functional languages (especially lazy ones) have been writing papers about identifying and exploiting thunks and lambdas that are used only once. However, it has proved difficult to achieve both power and simplicity in practice. In this paper, we describe a new, modular analysis for a higher order language, which is both simple and effective. We prove the analysis sound with respect to a standard call-by-need semantics, and present measurements of its use in a full-scale, state-of-the-art optimising compiler. The analysis finds many single-entry thunks and one-shot lambdas and enables a number of program optimisations. This paper extends our preceding conference publication (Sergey et al. 2014 Proceedings of the 41st Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages (POPL 2014). ACM, pp. 335–348) with proofs, expanded report on evaluation and a detailed examination of the factors causing the loss of precision in the analysis

Measuring neural net robustness with constraints

Despite having high accuracy, neural nets have been shown to be susceptible to adversarial examples, where a small perturbation to an input can cause it to become mislabeled. We propose metrics for measuring the robustness of a neural net and devise a novel algorithm for approximating these metrics based on an encoding of robustness as a linear program. We show how our metrics can be used to evaluate the robustness of deep neural nets with experiments on the MNIST and CIFAR-10 datasets. Our algorithm generates more informative estimates of robustness metrics compared to estimates based on existing algorithms. Furthermore, we show how existing approaches to improving robustness "overfit" to adversarial examples generated using a specific algorithm. Finally, we show that our techniques can be used to additionally improve neural net robustness both according to the metrics that we propose, but also according to previously proposed metrics

A Reflection on Types

The ability to perform type tests at runtime blurs the line between statically-typed and dynamically-checked languages. Recent developments in Haskell’s type system allow even programs that use reflection to themselves be statically typed, using a type-indexed runtime representation of types called \{}\textit{TypeRep}. As a result we can build dynamic types as an ordinary, statically-typed library, on top of \{}\textit{TypeRep} in an open-world context

Reasoning with the HERMIT: tool support for equational reasoning on GHC core programs

A benefit of pure functional programming is that it encourages equational reasoning. However, the Haskell language has lacked direct tool support for such reasoning. Consequently, reasoning about Haskell programs is either performed manually, or in another language that does provide tool support (e.g. Agda or Coq). HERMIT is a Haskell-specific toolkit designed to support equational reasoning and user-guided program transformation, and to do so as part of the GHC compilation pipeline. This paper describes HERMIT’s recently developed support for equational reasoning, and presents two case studies of HERMIT usage: checking that type-class laws hold for specific instance declarations, and mechanising textbook equational reasoning

HALO: Haskell to Logic through Denotational Semantics

Even well-typed programs can go wrong in modern functional languages, by encountering a pattern-match failure, or simply returning the wrong answer. An increasingly-popular response is to allow programmers to write contracts that express semantic properties, such as crash-freedom or some useful post-condition. We study the static verification of such contracts. Our main contribution is a novel translation to first-order logic of both Haskell programs, and contracts written in Haskell, all justified by denotational semantics. This translation enables us to prove that functions satisfy their contracts using an off-the-shelf first-order logic theorem prover

Type-based Declassification for Free

The short version of this paper is accepted in ICFEM 2020. 100 pagesInternational audienceThis work provides a study to demonstrate the potential of using off-the-shelf programming languages and their theories to build sound language-based-security tools. Our study focuses on information flow security encompassing declassification policies that allow us to express flexible security policies needed for practical requirements. We translate security policies, with declassification, into an interface for which an unmodified standard typechecker can be applied to a source program---if the program typechecks, it provably satisfies the policy. Our proof reduces security soundness---with declassification---to the mathematical foundation of data abstraction, Reynolds' abstraction theorem