In:
International Journal of Software Engineering and Knowledge Engineering, World Scientific Pub Co Pte Ltd, Vol. 31, No. 09 ( 2021-09), p. 1299-1327
Abstract:
Application Programming Interfaces (APIs) play an important role in modern software development. Developers interact with APIs on a daily basis and thus need to learn and memorize those APIs suitable for implementing the required functions. This can be a burden even for experienced developers since there exists a mass of available APIs. API recommendation techniques focus on assisting developers in selecting suitable APIs. However, existing API recommendation techniques have not taken the developers personal characteristics into account. As a result, they cannot provide developers with personalized API recommendation services. Meanwhile, they lack the support for self-defined APIs in the recommendation. To this end, we aim to propose a personalized API recommendation method that considers developers’ differences. Our API recommendation method is based on statistical language. We propose a model structure that combines the N-gram model and the long short-term memory (LSTM) neural network and train predictive models using API invoking sequences extracted from GitHub code repositories. A general language model trained on all sorts of code data is first acquired, based on which two personalized language models that recommend personalized library APIs and self-defined APIs are trained using the code data of the developer who needs personalized services. We evaluate our personalized API recommendation method on real-world developers, and the experimental results show that our approach achieves better accuracy in recommending both library APIs and self-defined APIs compared with the state-of-the-art. The experimental results also confirm the effectiveness of our hybrid model structure and the choice of the LSTM’s size.
Type of Medium:
Online Resource
ISSN:
0218-1940
,
1793-6403
DOI:
10.1142/S021819402150042X
Language:
English
Publisher:
World Scientific Pub Co Pte Ltd
Publication Date:
2021
