A comprehensive review of automatic programming methods

Automatic programming (AP) is one of the most attractive branches of artificial intelligence because it provides effective solutions to problems with limited knowledge in many different application areas. AP methods can be used to determine the effects of a system’s inputs on its outputs. Although there is increasing interest in solving many problems using these methods for a variety of applications, there is a lack of reviews that address the methods. Therefore, the goal of this paper is to provide a comprehensive literature review of AP methods. At the same time, we mention the main characteristics of the methods by grouping them according to how they represent solutions. We also try to give an outlook on the future of the field by highlighting possible bottlenecks and perspectives for the benefit of the researchers involved. •Automatic programming (AP) methods are grouped by the encoding similarity.•A comprehensive guideline is provided for AI researchers using AP methods.•Among the AP problems of interest are symbolic regression and feature selection.•Current metrics of AP methods and applications are presented.•Improvements, bottlenecks, and future directions of AP are discussed.