Information-gain computation

TL;DR

This paper introduces information-gain computation, a framework that uses adaptive evaluation strategies to efficiently derive algorithms from specifications by maximizing information gain, aiming to overcome performance limitations in declarative programming.

Contribution

It proposes a novel information-theoretic framework for adaptive evaluation that improves the efficiency of deriving algorithms from specifications.

Findings

Adaptive evaluation successfully evaluated a test program efficiently.

The framework suggests potential for bounding performance using information theory.

Preliminary results show promise for practical implementation.

Abstract

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.