  Understanding the correct API usage sequences is one of the most important
tasks for programmers when they work with unfamiliar libraries. However,
programmers often encounter obstacles to finding the appropriate information
due to either poor quality of API documentation or ineffective query-based
searching strategy. To help solve this issue, researchers have proposed various
methods to suggest the sequence of APIs given natural language queries
representing the information needs from programmers. Among such efforts, Gu et
al. adopted a deep learning method, in particular an RNN Encoder-Decoder
architecture, to perform this task and obtained promising results on common
APIs in Java. In this work, we aim to reproduce their results and apply the
same methods for APIs in Python. Additionally, we compare the performance with
a more recent Transformer-based method, i.e., CodeBERT, for the same task. Our
experiment reveals a clear drop in performance measures when careful data
cleaning is performed. Owing to the pretraining from a large number of source
code files and effective encoding technique, CodeBERT outperforms the method by
Gu et al., to a large extent.
