A Family Of Syntactic Logical Relations For The Semantics Of Haskell-Like Languages

Logical relations are a fundamental and powerful tool for reasoning about programs in languages with parametric polymorphism. Logical relations suitable for reasoning about observational behavior in polymorphic calculi supporting various programming language features have been introduced in recent years. Unfortunately, the calculi studied are typically idealized, and the results obtained for them over only partial insight into the impact of such features on observational behavior in implemented languages. In this paper we show how to bring reasoning via logical relations closer to bear on real languages by deriving results that are more pertinent to an intermediate language for the (mostly) lazy functional language Haskell like GHC Core. To provide a more ?ne-grained analysis of program behavior than is possible by reasoning about program equivalence alone, we work with an abstract notion of relating observational behavior of computations which has among its specializations both observational equivalence and observational approximation. We take selective strictness into account, and we consider the impact of different kinds of computational failure, e.g., divergence versus failed pattern matching, because such distinctions are significant in practice. Once distinguished, the relative de?nedness of different failure causes needs to be considered, because different orders here induce different observational relations on programs (including the choice between equivalence and approximation). Our main contribution is the construction of an entire family of logical relations, parameterized over a definedness order on failure causes, each member of which characterizes the corresponding observational relation. Although we deal with properties very much tied to types, we base our results on a type-erasing semantics since this is more faithful to actual implementations

Foundational Extensible Corecursion

This paper presents a formalized framework for defining corecursive functions safely in a total setting, based on corecursion up-to and relational parametricity. The end product is a general corecursor that allows corecursive (and even recursive) calls under well-behaved operations, including constructors. Corecursive functions that are well behaved can be registered as such, thereby increasing the corecursor's expressiveness. The metatheory is formalized in the Isabelle proof assistant and forms the core of a prototype tool. The corecursor is derived from first principles, without requiring new axioms or extensions of the logic

A Systematic Aspect-Oriented Refactoring and Testing Strategy, and its Application to JHotDraw

Aspect oriented programming aims at achieving better modularization for a system's crosscutting concerns in order to improve its key quality attributes, such as evolvability and reusability. Consequently, the adoption of aspect-oriented techniques in existing (legacy) software systems is of interest to remediate software aging. The refactoring of existing systems to employ aspect-orientation will be considerably eased by a systematic approach that will ensure a safe and consistent migration. In this paper, we propose a refactoring and testing strategy that supports such an approach and consider issues of behavior conservation and (incremental) integration of the aspect-oriented solution with the original system. The strategy is applied to the JHotDraw open source project and illustrated on a group of selected concerns. Finally, we abstract from the case study and present a number of generic refactorings which contribute to an incremental aspect-oriented refactoring process and associate particular types of crosscutting concerns to the model and features of the employed aspect language. The contributions of this paper are both in the area of supporting migration towards aspect-oriented solutions and supporting the development of aspect languages that are better suited for such migrations.Comment: 25 page

Logical relations for coherence of effect subtyping

A coercion semantics of a programming language with subtyping is typically defined on typing derivations rather than on typing judgments. To avoid semantic ambiguity, such a semantics is expected to be coherent, i.e., independent of the typing derivation for a given typing judgment. In this article we present heterogeneous, biorthogonal, step-indexed logical relations for establishing the coherence of coercion semantics of programming languages with subtyping. To illustrate the effectiveness of the proof method, we develop a proof of coherence of a type-directed, selective CPS translation from a typed call-by-value lambda calculus with delimited continuations and control-effect subtyping. The article is accompanied by a Coq formalization that relies on a novel shallow embedding of a logic for reasoning about step-indexing

Koka: Programming with Row Polymorphic Effect Types

We propose a programming model where effects are treated in a disciplined way, and where the potential side-effects of a function are apparent in its type signature. The type and effect of expressions can also be inferred automatically, and we describe a polymorphic type inference system based on Hindley-Milner style inference. A novel feature is that we support polymorphic effects through row-polymorphism using duplicate labels. Moreover, we show that our effects are not just syntactic labels but have a deep semantic connection to the program. For example, if an expression can be typed without an exn effect, then it will never throw an unhandled exception. Similar to Haskell's `runST` we show how we can safely encapsulate stateful operations. Through the state effect, we can also safely combine state with let-polymorphism without needing either imperative type variables or a syntactic value restriction. Finally, our system is implemented fully in a new language called Koka and has been used successfully on various small to medium-sized sample programs ranging from a Markdown processor to a tier-splitted chat application. You can try out Koka live at www.rise4fun.com/koka/tutorial.Comment: In Proceedings MSFP 2014, arXiv:1406.153

Learning-Assisted Automated Reasoning with Flyspeck

The considerable mathematical knowledge encoded by the Flyspeck project is combined with external automated theorem provers (ATPs) and machine-learning premise selection methods trained on the proofs, producing an AI system capable of answering a wide range of mathematical queries automatically. The performance of this architecture is evaluated in a bootstrapping scenario emulating the development of Flyspeck from axioms to the last theorem, each time using only the previous theorems and proofs. It is shown that 39% of the 14185 theorems could be proved in a push-button mode (without any high-level advice and user interaction) in 30 seconds of real time on a fourteen-CPU workstation. The necessary work involves: (i) an implementation of sound translations of the HOL Light logic to ATP formalisms: untyped first-order, polymorphic typed first-order, and typed higher-order, (ii) export of the dependency information from HOL Light and ATP proofs for the machine learners, and (iii) choice of suitable representations and methods for learning from previous proofs, and their integration as advisors with HOL Light. This work is described and discussed here, and an initial analysis of the body of proofs that were found fully automatically is provided

Structural Induction Principles for Functional Programmers

User defined recursive types are a fundamental feature of modern functional programming languages like Haskell, Clean, and the ML family of languages. Properties of programs defined by recursion on the structure of recursive types are generally proved by structural induction on the type. It is well known in the theorem proving community how to generate structural induction principles from data type declarations. These methods deserve to be better know in the functional programming community. Existing functional programming textbooks gloss over this material. And yet, if functional programmers do not know how to write down the structural induction principle for a new type - how are they supposed to reason about it? In this paper we describe an algorithm to generate structural induction principles from data type declarations. We also discuss how these methods are taught in the functional programming course at the University of Wyoming. A Haskell implementation of the algorithm is included in an appendix.Comment: In Proceedings TFPIE 2013, arXiv:1312.221

Relative Effect Declarations for Lightweight Effect-Polymorphism

Type-and-effect systems are a well-studied approach for reasoning about the computational behavior of programs. A major roadblock in adopting effect systems in popular languages is the tradeoff between expressiveness and verbosity. In this technical report, we present a syntactically lightweight but expressive system for annotating effect-polymorphic behavior of functions. The presented system is independent from any specific effect domains and can be embedded in an extensible type-and-effect system