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.
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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 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 |
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