On the difficulty of benchmarking inductive program synthesis methods

A variety of inductive program synthesis (IPS) techniques have recently been developed, emerging from di.erent areas of computer science. However, these techniques have not been adequately compared on general program synthesis problems. In this paper we compare several methods on problems requiring solution programs to handle various data types, control structures, and numbers of outputs. .e problem set also spans levels of abstraction; some would ordinarily be approached using machine code or assembly language, while others would ordinarily be approached using highlevel languages. .e presented comparisons are focused on the possibility of success; that is, on whether the system can produce a program that passes all tests, for all training and unseen testing inputs. .e compared systems are Flash Fill, MagicHaskeller, TerpreT, and two forms of genetic programming. .e two genetic programming methods chosen were PushGP and Grammar Guided Genetic Programming. .e results suggest that PushGP and, to an extent, TerpreT and Grammar Guided Genetic Programming are more capable of finding solutions than the others, albeit at a higher computational cost. A more salient observation is the dificulty of comparing these methods due to drastically di.erent intended applications, despite the common goal of program synthesis.