Dynamically Extensible Computational Reflection

已经在解释框架中探索了反射，并且倾向于假定程序解释的特定状态片段的可用性，甚至是当前源代码表达式。这项研究不是强调程序，而是强调计算作为反思的主要元素。这项研究有两个方面。第一个是形式化并继续开发一个不受基于解释器的限制的计算反射系统的理论。该系统支持引入多个用户定义的反射属性，这些属性可以独立设计并以任意方式组合。此外，在已经编译了要在其中使用它们的程序之后，可以引入新的反射属性。第二个方面是探索这种系统的多种用途。除了已经探索的简单但强大的用途外，该系统预计将用于语言功能的快速原型设计、协作软件开发和程序的静态分析，以进行密集的优化。以前在一流上下文编程中的研究已经调查了静态元级别上下文信息的含义和使用。这里的静态一词是指程序文本中可用的信息，而不是运行该程序。目前的研究通过探索动态元级上下文信息的含义和使用来扩展先前的工作，动态元级上下文信息是在运行过程的元级中可用的信息。与以前的工作一样，该项目强调模块化，允许同时利用许多不同类型的元级知识。***

Reflection has been explored in an interpreted framework and has tended to assume the availability of particular pieces of the state of a program's interpretation, even the current source code expression. Instead of programs, this research emphasizes computations as the primary elements of reflection. The research has two facets. The first is to formalize and continue the theoretical development of a system for computational reflection that is free from interpreter-based constraints. This system supports the introduction of multiple user-defined reflective properties that can be designed independently and combined in arbitrary fashion. Furthermore, new reflective properties can be introduced after the programs in which they are to be used have already been compiled. The second facet is to explore the many uses of such a system. In addition to the simple, but powerful, uses already explored, the system is expected to be useful for rapid prototyping of language features, collaborative software development, and static analysis of programs for intense optimization. Prior research in programming with first-class contexts has investigated the meaning and use of static meta-level contextual information. The word static here is intended to refer to that information available in the text of a program, without running it. The current research extends the previous work by exploring the meaning and use of dynamic meta-level contextual information, which is that information available in the meta-level of a running process. As in the previous work, this project emphasizes modularity, allowing many different kinds of meta-level knowledge to be utilized simultaneously. ***