Architecture and Knowledge Representation for Composable Inductive Programming
This work addresses the challenge of automating software development through inductive programming, but it appears incremental as it builds on existing architectures without demonstrating new breakthroughs.
The paper presents an update on the Zoea system, which tackles the problem of composable inductive programming by integrating multiple knowledge sources and using synthetic test cases for representation, but no concrete results or numbers are provided.
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.