  Large language models for code (LLM4Code), which demonstrate strong
performance (e.g., high accuracy) in processing source code, have significantly
transformed software engineering. Many studies separately investigate the
non-functional properties of LM4Code, but there is no systematic review of how
these properties are evaluated and enhanced. This paper fills this gap by
thoroughly examining 146 relevant studies, thereby presenting the first
systematic literature review to identify seven important properties beyond
accuracy, including robustness, security, privacy, explainability, efficiency,
and usability. We discuss the current state-of-the-art methods and trends,
identify gaps in existing research, and present promising directions for future
study.
