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GitHub Copilot, an extension for the Visual Studio Code developmentenvironment powered by the large-scale language model Codex, makes automaticprogram synthesis available for software developers. This model has beenextensively studied in the field of deep learning, however, a comparison togenetic programming, which is also known for its performance in automaticprogram synthesis, has not yet been carried out. In this paper, we evaluateGitHub Copilot on standard program synthesis benchmark problems and compare theachieved results with those from the genetic programming literature. Inaddition, we discuss the performance of both approaches. We find that theperformance of the two approaches on the benchmark problems is quite similar,however, in comparison to GitHub Copilot, the program synthesis approachesbased on genetic programming are not yet mature enough to support programmersin practical software development. Genetic programming usually needs a hugeamount of expensive hand-labeled training cases and takes too much time togenerate solutions. Furthermore, source code generated by genetic programmingapproaches is often bloated and difficult to understand. For future work onprogram synthesis with genetic programming, we suggest researchers to focus onimproving the execution time, readability, and usability.

Choose Your Programming Copilot: A Comparison of the Program Synthesis Performance of GitHub Copilot and Genetic Programming