Data Preparation for Deep Learning based Code Smell Detection: A Systematic Literature Review

TL;DR

This systematic review examines data preparation techniques for deep learning-based code smell detection, highlighting challenges, solutions, and best practices to improve dataset quality and effectiveness.

Contribution

It provides a comprehensive analysis of data preparation processes and offers practical recommendations for enhancing data quality in DL-based CSD methods.

Findings

Identified 36 relevant papers on DL-based CSD

Summarized seven key challenges and solutions in data preparation

Emphasized importance of data diversity, standardization, and accessibility

Abstract

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.