As Large Language Models become an ever more integral part of Software Engineering, often assisting developers on coding tasks, the need for an unbiased evaluation of their performance on such tasks grows [1]. Data smells [2] are reported to have an impact on a Large Language Model’s ability on such tasks [ 3]. Boilerplate code is considered to be a subcategory of said smells. In this paper, we investigate a specific type of this smell, boilerplate API usage patterns. We analyze their prevalence in The Heap dataset [1] and examine how they may bias reference-based evaluation of Large Language Models on code generation tasks. Our findings show that while this data smell is relatively rare, instances containing it are significantly easier for LLMs to predict. We attribute this to partial memorization of common boilerplate patterns, which inflates perceived model performance.