Semantic Scholar Open Access 2017 985 sitasi

A Survey of Machine Learning for Big Code and Naturalness

Miltiadis Allamanis Earl T. Barr Premkumar T. Devanbu Charles Sutton

Lihat Sumber DOI

Abstrak

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.

Topik & Kata Kunci

Computer Science

Penulis (4)

Miltiadis Allamanis

Earl T. Barr

Premkumar T. Devanbu

Charles Sutton

Format Sitasi

APA MLA BibTeX

Allamanis, M., Barr, E.T., Devanbu, P.T., Sutton, C. (2017). A Survey of Machine Learning for Big Code and Naturalness. https://doi.org/10.1145/3212695

Akses Cepat

Lihat di Sumber doi.org/10.1145/3212695

Informasi Jurnal

Tahun Terbit: 2017
Bahasa: en
Total Sitasi: 985×
Sumber Database: Semantic Scholar
DOI: 10.1145/3212695
Akses: Open Access ✓