Lambda the Ultimate

Extensible Effects -- An Alternative to Monad Transformers, by Oleg Kiselyov, Amr Sabry and Cameron Swords:

We design and implement a library that solves the long-standing problem of combining effects without imposing restrictions on their interactions (such as static ordering). Effects arise from interactions between a client and an effect handler (interpreter); interactions may vary throughout the program and dynamically adapt to execution conditions. Existing code that relies on monad transformers may be used with our library with minor changes, gaining efficiency over long monad stacks. In addition, our library has greater expressiveness, allowing for practical idioms that are inefficient, cumbersome, or outright impossible with monad transformers.

Our alternative to a monad transformer stack is a single monad, for the coroutine-like communication of a client with its handler. Its type reflects possible requests, i.e., possible effects of a computation. To support arbitrary effects and their combinations, requests are values of an extensible union type, which allows adding and, notably, subtracting summands. Extending and, upon handling, shrinking of the union of possible requests is reflected in its type, yielding a type-and-effect system for Haskell. The library is lightweight, generalizing the extensible exception handling to other effects and accurately tracking them in types.

A follow-up to Oleg's delimited continuation adaptation of Cartwright and Felleisen's work on Extensible Denotational Language Specifications, which is a promising alternative means of composing effects to the standard monad transformers.

This work embeds a user-extensible effect EDSL in Haskell by encoding all effects into a single effect monad using a novel open union type and the continuation monad. The encoding is very similar to recent work on Algebraic Effects and Handlers, and closely resembles a typed client-server interaction ala coroutines. This seems like a nice convergence of the topics covered in the algebraic effects thread and other recent work on effects, and it's more efficient than monad transformers to boot.

Ross Tate is calling for "Industry Endorsement" for his paper Mixed-Site Variance.

..this is an attempt to make industry experience admissible as evidence in academic settings, just like they do in industry settings.

Abstract:

Java introduced wildcards years ago. Wildcards were very expressive, and they were integral to updating the existing libraries to make use of generics. Unfortunately, wildcards were also complex and verbose, making them hard and inconvenient for programmers to adopt. Overall, while an impressive feature, wildcards are generally considered to be a failure. As such, many languages adopted a more restricted feature for generics, namely declaration-site variance, because designers believed its simplicity would make it easier for programmers to adopt. Indeed, declaration-site variance has been quite successful. However, it is also completely unhelpful for many designs, including many of those in the Java SDK. So, we have designed mixed-site variance, a careful combination of definition-site and use-site variance that avoids the failings of wildcards. We have been working with JetBrains to put this into practice by incorporating it into the design of their upcoming language, Kotlin. Here we exposit our design, our rationale, and our experiences.

Mention of it is also at Jetbrain's Kotlin blog.

Dependent Types for JavaScript, by Ravi Chugh, David Herman, Ranjit Jhala:

We present Dependent JavaScript (DJS), a statically-typed dialect of the imperative, object-oriented, dynamic language. DJS supports the particularly challenging features such as run-time type-tests, higher-order functions, extensible objects, prototype inheritance, and arrays through a combination of nested refinement types, strong updates to the heap, and heap unrolling to precisely track prototype hierarchies. With our implementation of DJS, we demonstrate that the type system is expressive enough to reason about a variety of tricky idioms found in small examples drawn from several sources, including the popular book JavaScript: The Good Parts and the SunSpider benchmark suite.

Some good progress on inferring types for a very dynamic language. Explicit type declarations are placed in comments that start with "/*:".

/*: x∶Top → {ν ∣ite Num(x) Num(ν) Bool(ν)} */
function negate(x) {
    if (typeof x == "number") { return 0 - x; }
    else { return !x; }
}

How OCaml type checker works -- or what polymorphism and garbage collection have in common

There is more to Hindley-Milner type inference than the Algorithm W. In 1988, Didier Rémy was looking to speed up the type inference in Caml and discovered an elegant method of type generalization. Not only it is fast, avoiding the scan of the type environment. It smoothly extends to catching of locally-declared types about to escape, to type-checking of universals and existentials, and to implementing MLF.

Alas, both the algorithm and its implementation in the OCaml type checker are little known and little documented. This page is to explain and popularize Rémy's algorithm, and to decipher a part of the OCaml type checker. The page also aims to preserve the history of Rémy's algorithm.

The attraction of the algorithm is its insight into type generalization as dependency tracking -- the same sort of tracking used in automated memory management such as regions and generational garbage collection. Generalization can be viewed as finding dominators in the type-annotated abstract syntax tree with edges for shared types. Fluet and Morrisett's type system for regions and MetaOCaml environment classifiers use the generalization of a type variable as a criterion of region containment. Uncannily, Rémy's algorithm views the region containment as a test if a type variable is generalizable.

As usual with Oleg, there's a lot going on here. Personally, I see parallels with "lambda with letrec" and "call-by-push-value," although making the connection with the latter takes some squinting through some of Levy's work other than his CBPV thesis. Study this to understand OCaml type inference and/or MLF, or for insights into region typing, or, as the title suggests, for suggestive analogies between polymorphism and garbage collection.

Video: Records, sums, cases, and exceptions: Row-polymorphism at work, Matthias Blume.

I will present the design of a programming language (called MLPolyR) whose type system makes significant use of row polymorphism (Rémy, 1991). MLPolyR (Blume et al. 2006) is a dialect of ML and provides extensible records as well as their exact dual, polymorphic sums with extensible first-class cases.

Found this to be an enjoyable and thorough overview of MLPolyR, a language created for a PL course that goes all-out on various dimensions of row polymorphism, resulting in a small yet powerful language. (previously)

Koka is a function-oriented programming language that seperates pure values from side-effecting computations, where the effect of every function is automatically inferred. Koka has many features that help programmers to easily change their data types and code organization correctly, while having a small language core with a familiar JavaScript like syntax.

Koka extends the idea of using row polymorphism to encode an effect system and the relations between them. Daan Leijen is the primary researcher behind it and his research was featured previously on LtU, mainly on row polymorphism in the Morrow Language.

So far there's no paper available on the language design, just the slides from a Lang.Next talk (which doesn't seem to have video available at Channel 9), but it's in the program for HOPE 2012.

How to Make Ad Hoc Proof Automation Less Ad Hoc
Georges Gonthier, Beta Ziliani, Aleksandar Nanevski, and Derek Dreyer, to appear in ICFP 2011

Most interactive theorem provers provide support for some form of user-customizable proof automation. In a number of popular systems, such as Coq and Isabelle, this automation is achieved primarily through tactics, which are programmed in a separate language from that of the prover’s base logic. While tactics are clearly useful in practice, they can be difficult to maintain and compose because, unlike lemmas, their behavior cannot be specified within the expressive type system of the prover itself.

We propose a novel approach to proof automation in Coq that allows the user to specify the behavior of custom automated routines in terms of Coq’s own type system. Our approach involves a sophisticated application of Coq’s canonical structures, which generalize Haskell type classes and facilitate a flexible style of dependently-typed logic programming. Specifically, just as Haskell type classes are used to infer the canonical implementation of an overloaded term at a given type, canonical structures can be used to infer the canonical proof of an overloaded lemma for a given instantiation of its parameters. We present a series of design patterns for canonical structure programming that enable one to carefully and predictably coax Coq’s type inference engine into triggering the execution of user-supplied algorithms during unification, and we illustrate these patterns through several realistic examples drawn from Hoare Type Theory. We assume no prior knowledge of Coq and describe the relevant aspects of Coq type inference from first principles.

If you've ever toyed with Coq but run into the difficulties that many encounter in trying to construct robust, comprehensible proof scripts using tactics, which manipulate the proof state and can leave you with the "ground" of the proof rather than the "figure," if you will, in addition to being fragile in the face of change, you may wish to give this a read. It frankly never would have occurred to me to try to turn Ltac scripts into lemmas at all. This is much more appealing than most other approaches to the subject I've seen.

Tool Demo: Scala-Virtualized

This paper describes Scala-Virtualized, which extends the Scala language and compiler with a small number of features that enable combining the benefits of shallow and deep embeddings of DSLs. We demonstrate our approach by showing how to embed three different domain-specific languages in Scala. Moreover, we summarize how others have been using our extended compiler in their own research and teaching. Supporting artifacts of our tool include web-based tutorials, nightly builds, and an Eclipse update site hosting an up-to-date version of the Scala IDE for Eclipse based on the Virtualized Scala compiler and standard library.

Scala has always had a quite good EDSL story thanks to implicits, dot- and paren-inference, and methods-as-operators. Lately there are proposals to provide it with both macros-in-the-camlp4-sense and support for multi-stage programming. This paper goes into some depth on the foundations of the latter subject.

Programming as collaborative reference (extended abstract and slides) by everybody's favorite PLT tag team, Oleg and Chung-chieh, from the Off-the-beaten track workshop affiliated with POPL 2012:

We argue that programming-language theory should face the pragmatic fact that humans develop software by interacting with computers in real time [hear, hear]. This interaction not only relies on but also bears on the core design and tools of programming languages, so it should not be left to Eclipse plug-ins and StackOverflow. We further illustrate how this interaction could be improved by drawing from existing research on natural-language dialogue, specifically on collaborative reference.

Overloading resolution is one immediate application. Here, collaborative reference can resolve the tension between fancy polymorphism and the need to write long type annotations everywhere. The user will submit a source code fragment with few or no annotations. If the compiler gets stuck -- e.g., because of ambiguous overloading -- it will ask the user for a hint. At the successful conclusion of the dialogue, the compiler saves all relevant context, in the form of inserted type annotations or as a proof tree attached to the source code. When revisiting the code later on, the same or a different programmer may ask the compiler questions about the inferred types and the reasons for chosen overloadings. We argue that such an interactive overloading resolution should be designed already in the type system (e.g., using oracles).

Kalani Thielen's The Algebra of Data, and the Calculus of Mutation is a very good explanation of ADTs, and also scratches the surfaces of Zippers:

With the spreading popularity of languages like F# and Haskell, many people are encountering the concept of an algebraic data type for the first time. When that term is produced without explanation, it almost invariably becomes a source of confusion. In what sense are data types algebraic? Is there a one-to-one correspondence between the structures of high-school algebra and the data types of Haskell? Could I create a polynomial data type? Do I have to remember the quadratic formula? Are the term-transformations of (say) differential calculus meaningful in the context of algebraic data types? Isn’t this all just a bunch of general abstract nonsense?

(hat tip to Daniel Yokomizo, who used to be an LtU member...)