{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T23:52:45Z","timestamp":1783641165733,"version":"3.55.0"},"reference-count":285,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2020,6,12]],"date-time":"2020-06-12T00:00:00Z","timestamp":1591920000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Comput. Surv."],"published-print":{"date-parts":[[2021,5,31]]},"abstract":"<jats:p>Deep Learning (DL) techniques for Natural Language Processing have been evolving remarkably fast. Recently, the DL advances in language modeling, machine translation, and paragraph understanding are so prominent that the potential of DL in Software Engineering cannot be overlooked, especially in the field of program learning. To facilitate further research and applications of DL in this field, we provide a comprehensive review to categorize and investigate existing DL methods for source code modeling and generation. To address the limitations of the traditional source code models, we formulate common program learning tasks under an encoder-decoder framework. After that, we introduce recent DL mechanisms suitable to solve such problems. Then, we present the state-of-the-art practices and discuss their challenges with some recommendations for practitioners and researchers as well.<\/jats:p>","DOI":"10.1145\/3383458","type":"journal-article","created":{"date-parts":[[2020,6,12]],"date-time":"2020-06-12T22:49:10Z","timestamp":1592002150000},"page":"1-38","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":129,"title":["Deep Learning for Source Code Modeling and Generation"],"prefix":"10.1145","volume":"53","author":[{"given":"Triet H. 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In Proceedings of the 11th International Conference on Internet Monitoring and Protection (ICIMP\u201916) . IARIA. Simon Aebersold, Krzysztof Kryszczuk, Sergio Paganoni, Bernhard Tellenbach, and Timothy Trowbridge. 2016. Detecting obfuscated JavaScripts using machine learning. In Proceedings of the 11th International Conference on Internet Monitoring and Protection (ICIMP\u201916). IARIA."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2635868.2635883"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2786805.2786849"},{"key":"e_1_2_1_5_1","volume-title":"Article 81 (July","author":"Allamanis Miltiadis","year":"2018","unstructured":"Miltiadis Allamanis , Earl T. Barr , Premkumar Devanbu , and Charles Sutton . 2018. A survey of machine learning for big code and naturalness. ACM Comput. Surv. 51, 4 , Article 81 (July 2018 ). Miltiadis Allamanis, Earl T. Barr, Premkumar Devanbu, and Charles Sutton. 2018. A survey of machine learning for big code and naturalness. ACM Comput. Surv. 51, 4, Article 81 (July 2018)."},{"key":"e_1_2_1_6_1","volume-title":"Proceedings of the International Conference on Learning Representations (ICLR\u201918)","author":"Allamanis Miltiadis","year":"2018","unstructured":"Miltiadis Allamanis , Marc Brockschmidt , and Mahmoud Khademi . 2018 . Learning to represent programs with graphs . In Proceedings of the International Conference on Learning Representations (ICLR\u201918) . Miltiadis Allamanis, Marc Brockschmidt, and Mahmoud Khademi. 2018. Learning to represent programs with graphs. 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