Using machine learning to generate source code is an active and highly important research area. It has been shown that genetic programming (GP) efficiently contributes to software repair. However, most of the published advances on applying GP to generate source code are limited to the C programming language.

This paper explores the use of genetic programming to generate objected oriented source code in a dynamically-typed setting. We found that GP is able to produce missing one-line statements with a precision of 51%. Our preliminary results contributes to the state of the art by indicating that GP may be effectively employed to generate source code for dynamically-typed object-oriented applications.