Journal
ACM COMPUTING SURVEYS
Volume 51, Issue 4, Pages -Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3212695
Keywords
Big code; code naturalness; software engineering tools; machine learning
Categories
Funding
- Microsoft Research Cambridge
- Engineering and Physical Sciences Research Council [EP/K024043/1, EP/P005659/1, EP/P005314/1]
- National Research Foundation [1414172]
- EPSRC [EP/P005314/1, EP/K024043/1, EP/P005659/1] Funding Source: UKRI
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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 the abundance of patterns of code. 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 eachmodel 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.
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