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Programming Language Processing (PLP) using machine learning has made vastimprovements in the past few years. Increasingly more people are interested inexploring this promising field. However, it is challenging for new researchersand developers to find the right components to construct their own machinelearning pipelines, given the diverse PLP tasks to be solved, the large numberof datasets and models being released, and the set of complex compilers ortools involved. To improve the findability, accessibility, interoperability andreusability (FAIRness) of machine learning components, we collect and analyze aset of representative papers in the domain of machine learning-based PLP. Wethen identify and characterize key concepts including PLP tasks, modelarchitectures and supportive tools. Finally, we show some example use cases ofleveraging the reusable components to construct machine learning pipelines tosolve a set of PLP tasks.

Finding Reusable Machine Learning Components to Build Programming Language Processing Pipelines