Analysis and evaluation of inductive programming systems in a higher-order framework

Abstract. In this paper we present a comparison of several inductive program-ming (IP) systems. IP addresses the problem of learning (recursive) programs from incomplete specifications, such as input/output examples. First, we intro-duce conditional higher-order term rewriting as a common framework for induc-tive program synthesis. Then we characterise the ILP system GOLEM and the inductive functional system MAGICHASKELLER within this framework. In con-sequence, we propose the inductive functional system IGOR II as a powerful and efficient approach to IP. Performance of all systems on a representative set of sample problems is evaluated and shows the strength of IGOR II.