What does focusing tell us about language design?

A blog post about Call-By-Push-Value by Rob Simmons: What does focusing tell us about language design?

I think that one of the key observations of focusing/CBPV is that programs are dealing with two different things - data and computation - and that we tend to get the most tripped up when we confuse the two.

  • Data is classified by data types (a.k.a. positive types). Data is defined by how it is constructed, and the way you use data is by pattern-matching against it.
  • Computation is classified by computation types (a.k.a. negative types). Computations are defined their eliminations - that is, by how they respond to signals/messages/pokes/arguments.

There are two things I want to talk about, and they're both recursive types: call-by-push-value has positive recursive types (which have the feel of inductive types and/or algebras and/or what we're used to as datatypes in functional languages) and negative recursive types (which have the feel of recursive, lazy records and/or "codata" whatever that is and/or coalgebras and/or what William Cook calls objects). Both positive and negative recursive types are treated by Paul Blain Levy in his thesis (section 5.3.2) and in the Call-By-Push Value book (section 4.2.2).

In particular, I want to claim that Call-By-Push-Value and focusing suggest two fundamental features that should be, and generally aren't (at least simultaneously) in modern programming languages:

  • Support for structured data with rich case analysis facilities (up to and beyond what are called views)
  • Support for recursive records and negative recursive types.

Previously on Rob's blog: Embracing and extending the Levy language; on LtU: Call by push-value, Levy: a Toy Call-by-Push-Value Language.

And let me also repeat CBPV's slogan, which is one of the finest in PL advocacy: Once the fine structure has been exposed, why ignore it?

Milawa on Jitawa: a Verified Theorem Prover


Aug 2010 - May 2011. Magnus Myreen has developed a verified Lisp system, named Jitawa, which can run Milawa. Our paper about this project was accepted to ITP 2011.

This is pretty interesting: Milawa was already "self-verifying," in the sense explained on the page. More recently, it's been made to run on a verified Lisp runtime, so that means the entire stack down to the X86_64 machine code is verified. Milawa itself is "ACL2-like," so it's not as interesting logically as, say, Isabelle or Coq, but it's far from a toy. Also, the Jitawa formalization apparently took place in HOL4, so you need to trust HOL4. Since HOL4 is an "LCF-like" system, you can do that to the extent that you trust the LCF process, but it doesn't satisfy the de Bruijn criterion in the same way Milawa or Coq do. Nevertheless, this seems like an important step toward the ultimate goal of having a stack that is verified "all the way down," as it were.

Vellvm: Formalizing the LLVM Intermediate Representation for Verified Program Transformations

Vellvm: Formalizing the LLVM Intermediate Representation for Verified Program Transformations by Jianzhou Zhao, Santosh Nagarakatte, Milo M. K. Martin, and Steve Zdancewic, POPL 2012

This paper presents Vellvm (verified LLVM), a framework for reasoning about programs expressed in LLVM's intermediate representation and transformations that operate on it. Vellvm provides a mechanized formal semantics of LLVM's intermediate representation, its type system, and properties of its SSA form. The framework is built using the Coq interactive theorem prover. It includes multiple operational semantics and proves relations among them to facilitate different reasoning styles and proof techniques.

To validate Vellvm's design, we extract an interpreter from the Coq formal semantics that can execute programs from LLVM test suite and thus be compared against LLVM reference implementations. To demonstrate Vellvm's practicality, we formalize and verify a previously proposed transformation that hardens C programs against spatial memory safety violations. Vellvm's tools allow us to extract a new, verified implementation of the transformation pass that plugs into the real LLVM infrastructure; its performance is competitive with the non-verified, ad-hoc original.

This obviously represents huge progress in marrying the theoretical benefits of tools like Coq with the practical benefits of tools like LLVM. We can only hope that this spurs further development in practical certified software development.

LTL types FRP

Alan Jeffrey (to appear 2012) LTL types FRP: Linear-time Temporal Logic Propositions as Types, Proofs as Functional Reactive Programs. To be presented at next year's Programming Languages meets Program Verification, (PLPV 2012).
Functional Reactive Programming (FRP) is a form of reactive programming whose model is pure functions over signals. FRP is often expressed in terms of arrows with loops, which is the type class for a Freyd category (that is a premonoidal category with a cartesian centre) equipped with a premonoidal trace. This type system suffices to define the dataflow structure of a reactive program, but does not express its temporal properties. In this paper, we show that Linear-time Temporal Logic (LTL) is a natural extension of the type system for FRP, which constrains the temporal behaviour of reactive programs. We show that a constructive LTL can be defined in a dependently typed functional language, and that reactive programs form proofs of constructive LTL properties. In particular, implication in LTL gives rise to stateless functions on streams, and the “constrains” modality gives rise to causal functions. We show that reactive programs form a partially traced monoidal category, and hence can be given as a form of arrows with loops, where the type system enforces that only decoupled functions can be looped.
Via Alan's G+ feed.

Extensible Programming with First-Class Cases

Extensible Programming with First-Class Cases, by Matthias Blume, Umut A. Acar, and Wonseok Chae:

We present language mechanisms for polymorphic, extensible records and their exact dual, polymorphic sums with extensible first-class cases. These features make it possible to easily extend existing code with new cases. In fact, such extensions do not require any changes to code that adheres to a particular programming style. Using that style, individual extensions can be written independently and later be composed to form larger components. These language mechanisms provide a solution to the expression problem.

We study the proposed mechanisms in the context of an implicitly typed, purely functional language PolyR. We give a type system for the language and provide rules for a 2-phase transformation: first into an explicitly typed λ-calculus with record polymorphism, and finally to efficient index-passing code. The first phase eliminates sums and cases by taking advantage of the duality with records.

We implement a version of PolyR extended with imperative features and pattern matching—we call this language MLPolyR. Programs in MLPolyR require no type annotations—the implementation employs a reconstruction algorithm to infer all types. The compiler generates machine code (currently for PowerPC) and optimizes the representation of sums by eliminating closures generated by the dual construction.

This is an elegant solution to the expression problem for languages with pattern matching. This paper was posted twice in LtU comments, but it definitely deserves its own story. Previous solutions to the exression problem are rather more involved, like Garrigue's use of recursion and polymorphic variants, because they lack support for extensible records which makes this solution so elegant.

Extensible records and first-class cases unify object-oriented and functional paradigms on a deeper level, since they enable first-class messages to be directly encoded. Add a sensible system for dynamics, and I argue you have most of the power people claim of dynamic languages without sacrificing the safety of static typing.

The Experimental Effectiveness of Mathematical Proof

The Experimental Effectiveness of Mathematical Proof

The aim of this paper is twofold. First, it is an attempt to give an answer to the famous essay of Eugene Wigner about the unreasonable effectiveness of mathematics in the natural sciences [25]. We will argue that mathematics are not only reasonably effective, but that they are also objectively effective in a sense that can be given a precise meaning. For that—and this is the second aim of this paper—we shall reconsider some aspects of Popper’s epistemology [23] in the light of recent advances of proof theory [8, 20], in order to clarify the interaction between pure mathematical reasoning (in the sense of a formal system) and the use of empirical hypotheses (in the sense of the natural sciences).

The technical contribution of this paper is the proof-theoretic analysis of the problem (already evoked in [23]) of the experimental modus tollens, that deals with the combination of a formal proof of the implication U ⇒ V with an experimental falsification of V to get an experimental falsification of U in the case where the formulæ U and V express empirical theories in a sense close to Popper’s. We propose a practical solution to this problem based on Krivine’s theory of classical realizability [20], and describe a simple procedure to extract from a formal proof of U ⇒ V (formalized in classical second-order arithmetic) and a falsifying instance of V a computer program that performs a finite sequence of tests on the empirical theory U until it finds (in finite time) a falsifying instance of U.

I thought I had already posted this, but apparently not.

Consider this paper the main gauntlet thrown down to those who insist that mathematical logic, the Curry-Howard Isomorphism, etc. might be fine for "algorithmic code" (as if there were any other kind) but is somehow inapplicable the moment a system interacts with the "real" or "outside" world (as if software weren't real).

Update: the author is Alexandre Miquel, and the citation is "Chapitre du livre Anachronismes logiques, à paraître dans la collection Logique, Langage, Sciences, Philosophie, aux Publications de la Sorbonne. Éd.: Myriam Quatrini et Samuel Tronçon, 2010."

A Semantic Model for Graphical User Interfaces

Nick Benton and Neel Krishnaswami, ICFP'11, A Semantic Model for Graphical User Interfaces:

We give a denotational model for graphical user interface (GUI) programming using the Cartesian closed category of ultrametric spaces. [..] We capture the arbitrariness of user input [..] [by a nondeterminism] “powerspace” monad.

Algebras for the powerspace monad yield a model of intuitionistic linear logic, which we exploit in the definition of a mixed linear/non-linear domain-specific language for writing GUI programs. The non-linear part of the language is used for writing reactive stream-processing functions whilst the linear sublanguage naturally captures the generativity and usage constraints on the various linear objects in GUIs, such as the elements of a DOM or scene graph.

We have implemented this DSL as an extension to OCaml, and give examples demonstrating that programs in this style can be short and readable.

This is an application of their (more squiggly) LICS'11 submission, Ultrametric Semantics of Reactive Programs. In both these cases, I find appealing the fact the semantic model led to a type system and a language that was tricky to find.


Opa is a new member in the family of languages aiming to make web programming transparent by automatically generating client-side Javascript and handling communication and session control. Opa is written in OCaml. A hierarchical database and web server are integrated with the language. The distribution model is based on a notion of a session, a construct roughly comparable to process definitions in the join-calculus or to concurrent objects in a number of formalisms.

A good place to start is here. And here you can find several example programs with accompanying source code.

Lightweight Monadic Programming in ML

Lightweight Monadic Programming in ML

Many useful programming constructions can be expressed as monads. Examples include probabilistic modeling, functional reactive programming, parsing, and information flow tracking, not to mention effectful functionality like state and I/O. In this paper, we present a type-based rewriting algorithm to make programming with arbitrary monads as easy as using ML's built-in support for state and I/O. Developers write programs using monadic values of type M t as if they were of type t, and our algorithm inserts the necessary binds, units, and monad-to-monad morphisms so that the program type checks. Our algorithm, based on Jones' qualified types, produces principal types. But principal types are sometimes problematic: the program's semantics could depend on the choice of instantiation when more than one instantiation is valid. In such situations we are able to simplify the types to remove any ambiguity but without adversely affecting typability; thus we can accept strictly more programs. Moreover, we have proved that this simplification is efficient (linear in the number of constraints) and coherent: while our algorithm induces a particular rewriting, all related rewritings will have the same semantics. We have implemented our approach for a core functional language and applied it successfully to simple examples from the domains listed above, which are used as illustrations throughout the paper.

This is an intriguing paper, with an implementation in about 2,000 lines of OCaml. I'm especially interested in its application to probabilistic computing, yielding a result related to Kiselyov and Shan's Hansei effort, but without requiring delimited continuations (not that there's anything wrong with delimited continuations). On a theoretical level, it's nice to see such a compelling example of what can be done once types are freed from the shackle of "describing how bits are laid out in memory" (another such compelling example, IMHO, is type-directed partial evaluation, but that's literally another story).

Levy: a Toy Call-by-Push-Value Language

Andrej Bauer's blog contains the PL Zoo project. In particular, the Levy language, a toy implementation of Paul Levy's CBPV in OCaml.

If you're curious about CBPV, this implementation might be a nice accompaniment to the book, or simply a hands on way to check it out.

It looks like an implementation of CBPV without sum and product types, with complex values, and without effects. I guess a more hands-on way to get to grips with CBPV would be to implement any of these missing features.

The posts are are 3 years old, but I've only just noticed them. The PL Zoo project was briefly mentioned here.

XML feed