A Catalog of Data Smells for Coding Tasks

Large Language Models (LLMs) are increasingly becoming fundamental in supporting software developers in coding tasks. The massive datasets used for training LLMs are often collected automatically, leading to the introduction of data smells. Previous work addressed this issue by using quality filters to handle some specific smells. Still, the literature lacks a systematic catalog of the data smells for coding tasks currently known. This article presents a Systematic Literature Review (SLR) focused on articles that introduce LLMs for coding tasks. We first extracted the quality filters adopted for training and testing such LLMs, inferred the root problem behind their adoption (data smells for coding tasks), and defined a taxonomy of such smells. Our results highlight discrepancies in the adoption of quality filters between pre-training and fine-tuning stages and across different coding tasks, shedding light on areas for improvement in LLM-based software development support.