  Code Smell Detection (CSD) plays a crucial role in improving software quality
and maintainability. And Deep Learning (DL) techniques have emerged as a
promising approach for CSD due to their superior performance. However, the
effectiveness of DL-based CSD methods heavily relies on the quality of the
training data. Despite its importance, little attention has been paid to
analyzing the data preparation process. This systematic literature review
analyzes the data preparation techniques used in DL-based CSD methods. We
identify 36 relevant papers published by December 2023 and provide a thorough
analysis of the critical considerations in constructing CSD datasets, including
data requirements, collection, labeling, and cleaning. We also summarize seven
primary challenges and corresponding solutions in the literature. Finally, we
offer actionable recommendations for preparing and accessing high-quality CSD
data, emphasizing the importance of data diversity, standardization, and
accessibility. This survey provides valuable insights for researchers and
practitioners to harness the full potential of DL techniques in CSD.
