PyART: Python API Recommendation in Real-Time

• Published in: Proceedings / International Conference on Software Engineering pp. 1634 - 1645
• Main Authors: He, Xincheng, Xu, Lei, Zhang, Xiangyu, Hao, Rui, Feng, Yang, Xu, Baowen
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2021
• Subjects: API recommendation; context analysis; data flow analysis; Feature extraction; Libraries; Python; real time recommendation; Real-time systems; Training; Visualization
• ISBN: 1665402962; 9781665402965
• ISSN: 1558-1225
• DOI: 10.1109/ICSE43902.2021.00145

API recommendation in real-time is challenging for dynamic languages like Python. Many existing API recommendation techniques are highly effective, but they mainly support static languages. A few Python IDEs provide API recommendation functionalities based on type inference and training on a large corpus of Python libraries and third-party libraries. As such, they may fail to recommend or make poor recommendations when type information is missing or target APIs are project-specific. In this paper, we propose a novel approach, PyART, to recommend APIs for Python programs in real-time. It features a light-weight analysis to derives so-called optimistic data-flow, which is neither sound nor complete, but simulates the local data-flow information humans can derive. It extracts three kinds of features: data-flow, token similarity, and token co-occurrence, in the context of the program point where a recommendation is solicited. A predictive model is trained on these features using the Random Forest algorithm. Evaluation on 8 popular Python projects demonstrates that PyART can provide effective API recommendations. When historic commits can be leveraged, which is the target scenario of a state-of-the-art tool ARIREC, our average top-1 accuracy is over 50% and average top-10 accuracy over 70%, outperforming APIREC and Intellicode (i.e., the recommendation component in Visual Studio) by 28.48%-39.05% for top-1 accuracy and 24.41%-30.49% for top-10 accuracy. In other applications such as when historic comments are not available and cross-project recommendation, PyART also shows better overall performance. The time to make a recommendation is less than a second on average, satisfying the real-time requirement.