Programming with "Big Code"

38 citations recorded by the source corpus.

How researchers see this paper

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A Survey of Machine Learning for Big Code and Naturalness (2017, background): “There have been short reviews summarizing the progress and the vision of the research area, from both software engineering [52] and programming languages perspectives [28, 195].”
Neural Program Repair by Jointly Learning to Localize and Repair (2019, methodology): “Primarily, we use ETH-Py150 2 , a public corpus of GitHub Python ﬁles extensively used in the literature (Raychev et al., 2016; Vechev & Yahav, 2016).”
Automatic programming: The open issue? (2019, unknown): “Such approaches might be used to leverage existing code (e.g., the idea of leveraging “big code” in repositories such as github [64]) or propose methods which might be adopted as search operators to transform code which is being generated.”
Neural Code Comprehension: A Learnable Representation of Code Semantics (2018, background): “[2], followed by several works leveraging embeddings to apply NLP techniques to programming languages [3, 61].”