A Survey on Natural Language Processing for Programming
This is an incremental survey that synthesizes existing research for researchers and practitioners in NLP and programming.
The paper systematically reviews natural language processing for programming, analyzing tasks, datasets, and models from structure-based and functionality-oriented perspectives to identify gaps and suggest future directions.
Natural language processing for programming aims to use NLP techniques to assist programming. It is increasingly prevalent for its effectiveness in improving productivity. Distinct from natural language, a programming language is highly structured and functional. Constructing a structure-based representation and a functionality-oriented algorithm is at the heart of program understanding and generation. In this paper, we conduct a systematic review covering tasks, datasets, evaluation methods, techniques, and models from the perspective of the structure-based and functionality-oriented property, aiming to understand the role of the two properties in each component. Based on the analysis, we illustrate unexplored areas and suggest potential directions for future work.