Abstract not found

Environment classifiers were proposed as a new approach to typing multi-stage languages. Safety was established in the simply-typed and let-polymorphic settings. While the motivation for classifiers was the feasibility of inference, this was in fact not established. This paper starts with the observation that inference for the full classifier-based system fails. We then identify a subset of the original system for which inference is possible. This subset, which uses implicit classifiers, retains significant expressivity (e.g. it can embed the calculi of Davies and Pfenning) and eliminates the need for classifier names in terms. Implicit classifiers were implemented in MetaOCaml, and no changes were needed to make an existing test suite acceptable by the new type checker.

Abstract. Meta-AspectJ (MAJ) is a language tool for generating AspectJ programs using code templates. MAJ itself is an extension of Java, so users can interleave arbitrary Java code with AspectJ code templates. MAJ is a structured meta-programming tool: a well-typed generator implies a syntactically correct generated program. MAJ promotes a methodology that combines aspect-oriented and generative programming. Potential applications range from implementing domain-specific languages with AspectJ as a back-end to enhancing AspectJ with more powerful general-purpose constructs. In addition to its practical value, MAJ offers valuable insights to meta-programming tool designers. It is a mature meta-programming tool for AspectJ (and, by extension, Java): a lot of emphasis has been placed on context-sensitive parsing and errorreporting. As a result, MAJ minimizes the number of meta-programming (quote/unquote) operators and uses type inference to reduce the need to remember type names for syntactic entities. 1

SafeGen is a meta-programming language for writing statically safe generators of Java programs. If a program generator written in SafeGen passes the checks of the SafeGen compiler, then the generator will only generate well-formed Java programs, for any generator input. In other words, statically checking the generator guarantees the correctness of any generated program, with respect to static checks commonly performed by a conventional compiler (including type safety, existence of a superclass, etc.). To achieve this guarantee, SafeGen supports only language primitives for reflection over an existing well-formed Java program, primitives for creating program fragments, and a restricted set of constructs for iteration, conditional actions, and name generation. SafeGen’s static checking algorithm is a combination of traditional type checking for Java, and a series of calls to a theorem prover to check the validity of first-order logical sentences constructed to represent well-formedness properties of the generated program under all inputs. The approach has worked quite well in our tests, providing proofs for correct generators or pointing out interesting bugs.

High-level programming languages offer significant expressivity but provide little or no guarantees about resource use. Resourcebounded languages --- such as hardware-description languages --- provide strong guarantees about the runtime behavior of computations but often lack mechanisms that allow programmers to write more structured, modular, and reusable programs. To overcome this basic tension in language design, recent work advocated the use of Resource-aware Programming (RAP) languages, which take into account the natural distinction between the development platform and the deployment platform for resource-constrained software.

Program generation is among the most promising techniques in the e#ort to increase the automation of programming tasks. In this paper, we discuss the potential impact and research value of program generation, we give examples of our research in the area, and we outline a future work direction that we consider most interesting. Specifically, we first discuss why program generators have significant applied potential. At the same time we argue that, as a research topic, meta-programming tools (i.e., language tools for writing program generators) may be of greater value. We then illustrate our views on generators and meta-programming tools with our latest work on the Meta-AspectJ metaprogramming language and the GOTECH generator. Finally, we examine the problem of statically determining the safety of a generator and present its intricacies. We limit our focus to one particular kind of guarantee for generated code---ensuring that the generated program is free of compile-time errors. We believe that this research direction will see significant attention and will make a di#erence in the mainstream adoption of meta-programming technology.

As processor complexity increases compilers tend to deliver suboptimal performance. Library generators such as ATLAS, FFTW and SPIRAL overcome this issue by empirically searching in the space of possible program versions for the one that performs the best. Empirical search can also be applied by programmers, but because they lack a tool to automate the process, programmers need to manually re-write the application in terms of several parameters whose best value will be determined by the empirical search in the target machine.

The quality of compiler-optimized code for high-performance applications lags way behind what optimization and domain experts can achieve by hand. This paper explores in-between solutions, besides fully automatic and fully-manual code optimization. This work discusses how generative approaches can help the design and optimization of supercomputing applications. It outlines early results and research directions, using MetaOCaml for the design of a generative tool-box to design portable optimized code. We also identify some limitations the MetaOCaml system. We finally present and advocate for an offshoring approach to bring high-level and safe metaprogramming to imperative languages.

Abstract. Domain-specific languages (DSLs) are increasingly used as embedded languages within general-purpose host languages. DSLs provide a compact, dedicated syntax for specifying parts of an application related to specialized domains. Unfortunately, such language extensions typically do not integrate well with the development tools of the host language. Editors, compilers and debuggers are either unaware of the extensions, or must be adapted at a non-trivial cost. We present a novel approach to embed DSLs into an existing host language by leveraging the underlying representation of the host language used by these tools. Helvetia is an extensible system that intercepts the compilation pipeline of the Smalltalk host language to seamlessly integrate language extensions. We validate our approach by case studies that demonstrate three fundamentally different ways to extend or adapt the host language syntax and semantics. 1

Abstract. We present MJ: a language for specifying general classes whose members are produced by iterating over members of other classes. We call this technique “class morphing ” or just “morphing”. Morphing extends the notion of genericity so that not only types of methods and fields, but also the structure of a class can vary according to type variables. This offers the ability to express common programming patterns in a highly generic way that is otherwise not supported by conventional techniques. For instance, morphing lets us write generic proxies (i.e., classes that can be parameterized with another class and export the same public methods as that class); default implementations (e.g., a generic do-nothing type, configurable for any interface); semantic extensions (e.g., specialized behavior for methods that declare a certain annotation); and more. MJ’s hallmark feature is that, despite its emphasis on generality, it allows modular type checking: an MJ class can be checked independently of its uses. Thus, the possibility of supplying a type parameter that will lead to invalid code is detected early—an invaluable feature for highly general components that will be statically instantiated by other programmers. 1