User and Technical Perspectives of Controllable Code Generation

Abstract

Large language models (LLM) such as OpenAI Codex are increasingly being applied to software engineering tasks to generate code for a variety of purposes including code authoring and code translation. Human-centered research in this domain has revealed that software engineers would like the ability to influence or control the properties of generated code so as to optimize output for their particular code base and application needs. In this work, we explore user requirements for controllable code generation and show that human-written code is more optimal than standard beam-search code outputs from a large language model.