6533b831fe1ef96bd1299798

RESEARCH PRODUCT

Using knowledge of human-generated code to bias the search in program synthesis with grammatical evolution

subject

description

Recent studies show that program synthesis with GE produces code that has different structure compared to human-generated code, e.g., loops and conditions are hardly used. In this article, we extract knowledge from human-generated code to guide evolutionary search. We use a large code-corpus that was mined from the open software repository service GitHub and measure software metrics and properties describing the code-base. We use this knowledge to guide the search by incorporating a new selection scheme. Our new selection scheme favors programs that are structurally similar to the programs in the GitHub code-base. We find noticeable evidence that software metrics can help in guiding evolutionary search.