The Composability of Intermediate Values in Composable Inductive Programming

TL;DR

This study investigates how intermediate values and test cases influence the size and composability of programs generated through composable inductive programming, revealing linear relationships and trade-offs.

Contribution

It provides empirical evidence on the relationships between program size, intermediate values, and test cases in CIP, highlighting their linear correlations and trade-offs.

Findings

Linear relationship between intermediate values and program size

Linear relationship between test cases and program size

Trade-offs between test cases and intermediate values as size increases

Abstract

It is believed that mechanisms including intermediate values enable composable inductive programming (CIP) to be used to produce software of any size. We present the results of a study that investigated the relationships between program size, the number of intermediate values and the number of test cases used to specify programs using CIP. In the study 96,000 programs of various sizes were randomly generated, decomposed into fragments and transformed into test cases. The test cases were then used to regenerate new versions of the original programs using Zoea. The results show linear relationships between the number of intermediate values and regenerated program size, and between the number of test cases and regenerated program size within the size range studied. In addition, as program size increases there is increasing scope for trading off the number of test cases against the number of intermediate values and vice versa.