寻找可复用的机器学习组件以构建编程语言处理流水线
Finding Reusable Machine Learning Components to Build Programming Language Processing Pipelines
AI总结：
针对编程语言处理领域新从业者难寻合适机器学习组件搭建流水线的问题，研究通过分析代表性论文梳理核心概念，提升组件FAIR特性，并给出复用组件搭建流水线的示例。
摘要：
近年来，基于机器学习的编程语言处理（Programming Language Processing, PLP）领域取得了长足进展，越来越多的人有意探索这一前景广阔的领域。然而，由于待解决的PLP任务种类繁多、发布的数据集和模型数量庞大，且涉及一系列复杂的编译器或工具，新入行的研究人员和开发人员很难找到合适的组件来搭建自己的机器学习流水线。为提升机器学习组件的可发现性、可访问性、互操作性与可复用性（即FAIR原则），我们收集并分析了基于机器学习的PLP领域的一批代表性论文，随后识别并梳理了PLP任务、模型架构、支撑工具等核心概念的特征。最后，我们展示了若干利用可复用组件搭建机器学习流水线以解决一系列PLP任务的示例用例。
英文摘要：
Programming Language Processing (PLP) using machine learning has made vast improvements in the past few years. Increasingly more people are interested in exploring this promising field. However, it is challenging for new researchers and developers to find the right components to construct their own machine learning pipelines, given the diverse PLP tasks to be solved, the large number of datasets and models being released, and the set of complex compilers or tools involved. To improve the findability, accessibility, interoperability and reusability (FAIRness) of machine learning components, we collect and analyze a set of representative papers in the domain of machine learning-based PLP. We then identify and characterize key concepts including PLP tasks, model architectures and supportive tools. Finally, we show some example use cases of leveraging the reusable components to construct machine learning pipelines to solve a set of PLP tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
