Introduces a taxonomy of nine LLM code smells, a static detection tool, and reports 73.5% prevalence with 91.3% precision and 71.8% recall across 692 projects.
https://arxiv.org/abs/2504.08619
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A rapid review of fairness in LLM-enabled multi-agent systems for the software development lifecycle concludes that the field lacks standardized evaluations, broad coverage, and effective governance, leaving it unprepared for deployable fair systems.
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LLM Code Smells: A Taxonomy and Detection Approach
Introduces a taxonomy of nine LLM code smells, a static detection tool, and reports 73.5% prevalence with 91.3% precision and 71.8% recall across 692 projects.
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Fairness in Multi-Agent Systems for Software Engineering: An SDLC-Oriented Rapid Review
A rapid review of fairness in LLM-enabled multi-agent systems for the software development lifecycle concludes that the field lacks standardized evaluations, broad coverage, and effective governance, leaving it unprepared for deployable fair systems.