  Generating meaningful assert statements is one of the key challenges in
automated test case generation, which requires understanding the intended
functionality of the tested code. Recently, deep learning-based models have
shown promise in improving the performance of assert statement generation.
However, existing models only rely on the test prefixes along with their
corresponding focal methods, yet ignore the developer-written summarization.
Based on our observations, the summarization contents usually express the
intended program behavior or contain parameters that will appear directly in
the assert statement. Such information will help existing models address their
current inability to accurately predict assert statements. This paper presents
a novel summarization-guided approach for automatically generating assert
statements. To derive generic representations for natural language (i.e.,
summarization) and programming language (i.e., test prefixes and focal
methods), we leverage a pre-trained language model as the reference
architecture and fine-tune it on the task of assert statement generation. To
the best of our knowledge, the proposed approach makes the first attempt to
leverage the summarization of focal methods as the guidance for making the
generated assert statements more accurate. We demonstrate the effectiveness of
our approach on two real-world datasets when compared with state-of-the-art
models.
