信息增益计算
Information-gain computation
摘要：
尽管有大量的激励措施，软件的正确性仍然是一个难以捉摸的目标。声明式编程技术，其中算法是从所需行为的规范中派生出来的，提供了解决这个问题的希望，因为在编程中，在规范而不是算法方面存在复杂性的组合降低，并且可以直接在规范中表达和执行任意所需属性。然而，性能上的限制阻止了使用声明性规范的编程成为通用编程的主流技术。为了解决性能瓶颈，从规范中派生的算法，我提出了信息增益计算，一个框架，自适应评估策略是用来有效地执行搜索，派生算法，提供有关查询的信息最直接。在这个框架内，压缩搜索空间的机会，这表明，信息理论的界限，这样一个系统的性能可能会阐明和系统设计，以实现它们。在一个简单的测试程序的自适应评估的初步实证研究，评估策略适应成功地评估一个查询有效。
英文摘要：
Despite large incentives, ecorrectness in software remains an elusive goal. Declarative programming techniques, where algorithms are derived from a specification of the desired behavior, offer hope to address this problem, since there is a combinatorial reduction in complexity in programming in terms of specifications instead of algorithms, and arbitrary desired properties can be expressed and enforced in specifications directly. However, limitations on performance have prevented programming with declarative specifications from becoming a mainstream technique for general-purpose programming. To address the performance bottleneck in deriving an algorithm from a specification, I propose information-gain computation, a framework where an adaptive evaluation strategy is used to efficiently perform a search which derives algorithms that provide information about a query most directly. Within this framework, opportunities to compress the search space present themselves, which suggest that information-theoretic bounds on the performance of such a system might be articulated and a system designed to achieve them. In a preliminary empirical study of adaptive evaluation for a simple test program, the evaluation strategy adapts successfully to evaluate a query efficiently.
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