Join point interception (JPI), is considered an important cornerstone of aspect-oriented languages. However, we claim that JPI alone does not suffice for a modular structuring of aspects. We propose CAESAR 1, a model for aspect-oriented programming with a higher-level module concept on top of JPI, which enables reuse and componentization of aspects, allows us to use aspects polymorphically, and introduces a novel concept for dynamic aspect deployment.

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In many projects, lexical preprocessors are used to manage different variants of the project (using conditional compilation) and to define compile-time code transformations (using macros). Unfortunately, while being a simple way to implement variability, conditional compilation and lexical macros hinder automatic analysis, even though such analysis is urgently needed to combat variability-induced complexity. To analyze code with its variability, we need to parse it without preprocessing it. However, current parsing solutions use unsound heuristics, support only a subset of the language, or suffer from exponential explosion. As part of the TypeChef project, we contribute a novel variability-aware parser that can parse almost all unpreprocessed code without heuristics in practicable time. Beyond the obvious task of detecting syntax errors, our parser paves the road for further analysis, such as variability-aware type checking. We implement variability-aware parsers for Java and GNU C and demonstrate practicability by parsing the product line MobileMedia and the entire X86 architecture of the Linux kernel with 6065 variable features.

A widespread implementation approach for the join point mechanism of aspect-oriented languages is to instrument areas in code that match the static part of pointcut designators, inserting dynamic checks for that part of matching that depends on run-time conditions, if needed. For performance reasons, such dynamic checks should be avoided whenever possible. One way to do so is to postpone weaving of advice calls until run-time, when conditions determining the emergence of join points hold. This calls for fluid code---code that adapts itself to the join point emergence at run-time, and suggests that AOP concepts should be integrated into the execution model underlying a VM. In this paper, we present first steps toward such an integration in Steamloom, an extension of IBM's Jikes Research Virtual Machine. Steamloom is fairly restricted, but our initial experimental results indicate that aspect-aware VMs and fluid code are promising w.r.t performance. While the focus in this paper is on performance, there are other advantages of aspect-aware VMs to be investigated in the future.Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.

Abstract. In aspect-oriented programming, pointcuts are used to describe crosscutting structure. Pointcuts that abstract over irrelevant implementation details are clearly desired to better support maintainability and modular reasoning.We present an analysis which shows that current pointcut languages support localization of crosscutting concerns but are problematic with respect to information hiding. To cope with the problem, we present a pointcut language that exploits information from different models of program semantics, such as the execution trace, the syntax tree, the heap, static type system, etc., and supports abstraction mechanisms analogous to functional abstraction. We show how this raises the abstraction level and modularity of pointcuts and present first steps toward an efficient implementation by means of a static analysis technique.

In many projects, lexical preprocessors are used to manage different variants of the project (using conditional compilation) and to define compile-time code transformations (using macros). Unfortunately, while being a simple way to implement variability, conditional compilation and lexical macros hinder automatic analysis, even though such analysis is urgently needed to combat variability-induced complexity. To analyze code with its variability, we need to parse it without preprocessing it. However, current parsing solutions use unsound heuristics, support only a subset of the language, or suffer from exponential explosion. As part of the TypeChef project, we contribute a novel variability-aware parser that can parse almost all unpreprocessed code without heuristics in practicable time. Beyond the obvious task of detecting syntax errors, our parser paves the road for further analysis, such as variability-aware type checking. We implement variability-aware parsers for Java and GNU C and demonstrate practicability by parsing the product line MobileMedia and the entire X86 architecture of the Linux kernel with 6065 variable features.

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The influential pure embedding methodology of embedding domainspecific languages (DSLs) as libraries into a general-purpose host language forces the DSL designer to commit to a single semantics. This precludes the subsequent addition of compilation, optimization or domain-specific analyses. We propose polymorphic embedding of DSLs, where many different interpretations of a DSL can be provided as reusable components, and show how polymorphic embedding can be realized in the programming language Scala. With polymorphic embedding, the static type-safety, modularity, composability and rapid prototyping of pure embedding are reconciled with the flexibility attainable by external toolchains.