%0 Journal Article %1 Allamanis:18:SMLBC %A Allamanis, Miltiadis %A Barr, Earl T. %A Devanbu, Premkumar %A Sutton, Charles %D 2018 %I ACM %J ACM Comput. Surv. %K Big code engineering learning machine naturalness software tools %N 4 %R 10.1145/3212695 %T A Survey of Machine Learning for Big Code and Naturalness %V 51 %X 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.

@article{Allamanis:18:SMLBC, 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.}, added-at = {2021-03-18T00:53:16.000+0100}, articleno = {81}, author = {Allamanis, Miltiadis and Barr, Earl T. and Devanbu, Premkumar and Sutton, Charles}, biburl = {https://www.bibsonomy.org/bibtex/21ef6aef501b1c89c1ee5b09caa95d230/gron}, doi = {10.1145/3212695}, interhash = {4a8d4d44007caa350917375411a9c8be}, intrahash = {1ef6aef501b1c89c1ee5b09caa95d230}, issn = {0360-0300}, issue_date = {September 2018}, journal = {ACM Comput. Surv.}, keywords = {Big code engineering learning machine naturalness software tools}, month = jul, number = 4, numpages = {37}, publisher = {ACM}, timestamp = {2021-03-18T00:53:16.000+0100}, title = {{A Survey of Machine Learning for Big Code and Naturalness}}, volume = 51, year = 2018 }