Architecture and Knowledge Representation for Composable Inductive Programming

TL;DR

This paper updates the architecture of Zoea, a knowledge-based system for composable inductive programming, emphasizing its black-board architecture and integration of diverse knowledge sources for reasoning.

Contribution

It introduces a modern black-board architecture for Zoea and details how synthetic test cases serve as a universal knowledge representation for reasoning strategies.

Findings

Zoea integrates multiple knowledge sources for inductive programming.

Synthetic test cases support various reasoning strategies.

The system's architecture enhances composability and reasoning capabilities.

Abstract

We present an update on the current architecture of the Zoea knowledge-based, Composable Inductive Programming system. The Zoea compiler is built using a modern variant of the black-board architecture. Zoea integrates a large number of knowledge sources that encode different aspects of programming language and software development expertise. We describe the use of synthetic test cases as a ubiquitous form of knowledge and hypothesis representation that sup-ports a variety of reasoning strategies. Some future plans are also outlined.