Work

A Survey of Machine Learning for Big Code and Naturalness

Year: 2018

Type: article

Abstract: 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 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.

Source: ACM Computing Surveys

DOI: 10.1145/3212695

Authors: Miltiadis Allamanis, Earl T. Barr, Premkumar Devanbu, Charles Sutton

Institutions: Microsoft Research (United Kingdom), University College London, University of California, Davis, The Alan Turing Institute, University of Edinburgh

Language: English

Topic: Software Engineering Research

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funders: National Science Foundation, Microsoft Research, National Research Foundation, Engineering and Physical Sciences Research Council