UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

A survey of machine learning for big code and naturalness

Allamanis, M; Barr, ET; Devanbu, P; Sutton, C; (2018) A survey of machine learning for big code and naturalness. ACM Computing Surveys , 51 (4) , Article 81. 10.1145/3212695. Green open access

[img]
Preview
Text
1709.06182.pdf - Accepted version

Download (529kB) | Preview

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.

Type: Article
Title: A survey of machine learning for big code and naturalness
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/3212695
Publisher version: https://doi.org/10.1145/3212695
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Big code, code naturalness, software engineering tools, machine learning
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10060012
Downloads since deposit
37Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item