Refactoring smelly Java code and adding context, role, or token-limit prompts cut LLM chain-of-thought token use by roughly 15-50% in this study, but the 'no quality loss' claim rests only on shallow similarity metrics.
Data Preparation for Deep Learning based Code Smell Detection: A Systematic Literature Review
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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.
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Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency
Refactoring smelly Java code and adding context, role, or token-limit prompts cut LLM chain-of-thought token use by roughly 15-50% in this study, but the 'no quality loss' claim rests only on shallow similarity metrics.