PyART:实时Python API推荐

Xincheng He, Lei Xu, X. Zhang, Rui Hao, Yang Feng, Baowen Xu
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引用次数: 0

摘要

这是题为“PyART:实时Python API推荐”的论文的研究成果。PyART是Python的实时API推荐工具,它包括两个主要功能:数据流分析和针对不完整和完整Python代码上下文的实时API推荐。与经典工具相比,PyART有两个重要的特点:它能够处理实时推荐场景,并为动态语言提供数据流分析和API推荐。在实时推荐场景中,由于语法的不完备,经典工具往往无法进行静态分析。而Python语言的动态特性也给类型推断和API推荐带来了挑战。与传统工具不同,PyART派生的乐观数据流既不可靠也不完整,但足以进行API推荐,并且收集起来成本低廉,并基于新颖的候选收集、上下文分析和特征学习技术提供实时API推荐。PyART的工件评估实验主要包括三个方面:数据流分析、项目内API推荐和跨项目API推荐。我们假设工件的用户能够使用Linux Ubuntu操作系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PyART: Python API Recommendation in Real-Time
This is the research artifact of the paper titled 'PyART: Python API Recommendation in Real-Time'. PyART is a real-time API recommendation tool for Python, which includes two main functions: data-flow analysis and real-time API recommendation for both incomplete and complete Python code context. Compared to classical tools, PyART has two important particularities: it is able to work on real-time recommendation scenario, and it provides data-flow analysis and API recommendation for dynamic language. Classical tools often fail to make static analysis in real-time recommendation scenario, due to the incompletion of syntax. And the dynamic features of Python language also bring challenges to type inference and API recommendation. Different from classical tools, PyART derives optimistic data-flow that is neither sound nor complete but sufficient for API recommendation and cost-effective to collect, and provides real-time API recommendations based on novel candidate collection, context analysis and feature learning techniques. The artifact evaluation experiments of PyART include three main aspects: data-flow analysis, intra-project API recommendation and across-project API recommendation. We assume users of the artifact is able to use Linux Ubuntu Operating System.
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