  Modern language models (LMs) have been successfully employed in source code
generation and understanding, leading to a significant increase in research
focused on learning-based code intelligence, such as automated bug repair, and
test case generation. Despite their great potential, language models for code
intelligence (LM4Code) are susceptible to potential pitfalls, which hinder
realistic performance and further impact their reliability and applicability in
real-world deployment. Such challenges drive the need for a comprehensive
understanding - not just identifying these issues but delving into their
possible implications and existing solutions to build more reliable language
models tailored to code intelligence. Based on a well-defined systematic
research approach, we conducted an extensive literature review to uncover the
pitfalls inherent in LM4Code. Finally, 67 primary studies from top-tier venues
have been identified. After carefully examining these studies, we designed a
taxonomy of pitfalls in LM4Code research and conducted a systematic study to
summarize the issues, implications, current solutions, and challenges of
different pitfalls for LM4Code systems. We developed a comprehensive
classification scheme that dissects pitfalls across four crucial aspects: data
collection and labeling, system design and learning, performance evaluation,
and deployment and maintenance. Through this study, we aim to provide a roadmap
for researchers and practitioners, facilitating their understanding and
utilization of LM4Code in reliable and trustworthy ways.
