Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Survey of Machine Learning for Big Code and Naturalness   Research at the intersection of machine learning, programming languages, and
software engineering has recently taken important steps in proposing learnable
probabilistic models of source code that exploit code's abundance of patterns.
In this article, we survey this work. We contrast programming languages against
natural languages and discuss how these similarities and differences drive the
design of probabilistic models. We present a taxonomy based on the underlying
design principles of each model and use it to navigate the literature. Then, we
review how researchers have adapted these models to application areas and
discuss cross-cutting and application-specific challenges and opportunities.

25
The categories of this paper are: patt-sol
2
The categories of this paper are: math
27
The categories of this paper are: comp-gas
10
The categories of this paper are: q-bio
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
19
The categories of this paper are: eess
4
The categories of this paper are: cond-mat
21
The categories of this paper are: adap-org
6
The categories of this paper are: astro-ph
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
26
The categories of this paper are: bayes-an
14
The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
29
The categories of this paper are: funct-an