On the effectiveness of pretrained models for API learning
Mohammad Abdul Hadi, Imam Nur Bani Yusuf, Ferdian Thung, Kien Gia Luong, Lingxiao Jiang, Fatemeh H. Fard, David Lo · 2022
Developers frequently use APIs to implement certain functionalities, such as parsing Excel Files, reading and writing text files line by line, etc. Developers can greatly benefit from automatic API usage sequence generation based on natural language queries for building applications in a faster and cleaner manner. Existing approaches utilize information retrieval models to search for matching API sequences given a query or use RNN-based encoder-decoder to generate API sequences. As it stands, the first approach treats queries and API names as bags of words. It lacks deep comprehension of the semantics of the queries. The latter approach adapts a neural language model to encode a user query into a fixed-length context vector and generate API sequences from the context vector.