Exploring Language Model's Code Generation Ability with Auxiliary
  Functions
Seonghyeon Lee Sanghwan Jang Seongbo Jang Dongha Lee Hwanjo Yu
Auxiliary function is a helpful component to improve language model's code
generation ability. However, a systematic exploration of how they affect has
yet to be done. In this work, we comprehensively evaluate the ability to
utilize auxiliary functions encoded in recent code-pretrained language models.
First, we construct a human-crafted evaluation set, called HumanExtension,
which contains examples of two functions where one function assists the other.
With HumanExtension, we design several experiments to examine their ability in
a multifaceted way. Our evaluation processes enable a comprehensive
understanding of including auxiliary functions in the prompt in terms of
effectiveness and robustness. An additional implementation style analysis
captures the models' various implementation patterns when they access the
auxiliary function. Through this analysis, we discover the models' promising
ability to utilize auxiliary functions including their self-improving behavior
by implementing the two functions step-by-step. However, our analysis also
reveals the model's underutilized behavior to call the auxiliary function,
suggesting the future direction to enhance their implementation by eliciting
the auxiliary function call ability encoded in the models. We release our code
and dataset to facilitate this research direction.