{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T13:41:12Z","timestamp":1763646072549,"version":"3.45.0"},"reference-count":249,"publisher":"Association for Computing Machinery (ACM)","issue":"5","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Comput. Surv."],"published-print":{"date-parts":[[2026,4,30]]},"abstract":"<jats:p>\n                    In the contemporary era, deep learning (DL) is increasingly recognized as a promising approach for enabling and optimizing various techniques, notably in the domain of\n                    <jats:italic toggle=\"yes\">DL for code<\/jats:italic>\n                    (software programs). In essence, deep learning is mainly representation learning, which naturally holds for this domain. Thus, at the core of DL for code is deep representation learning for programs. The learned program representations can then be applied to various coding-related tasks, such as detecting vulnerabilities, providing recommendations for API usage, and extracting semantic and syntactic insights from extensive code lines. This is achieved by harnessing deep neural network\n                    <jats:italic toggle=\"yes\">architectures<\/jats:italic>\n                    and deep-learning\n                    <jats:italic toggle=\"yes\">algorithms<\/jats:italic>\n                    that take programs as\n                    <jats:italic toggle=\"yes\">inputs<\/jats:italic>\n                    , serving various software engineering\n                    <jats:italic toggle=\"yes\">applications<\/jats:italic>\n                    .\n                  <\/jats:p>\n                  <jats:p>\n                    In this article, we conduct a systematic literature search to review studies pertaining to the representation of programs using deep learning approaches and their corresponding applications. Our search yielded 178 primary studies published between 2017 and 2023. Through these studies in the latest literature, we provide a systematization of knowledge in deep learning representation of programs, concerning the\n                    <jats:italic toggle=\"yes\">raw inputs<\/jats:italic>\n                    to the learning pipeline,\n                    <jats:italic toggle=\"yes\">neural network architecture<\/jats:italic>\n                    employed, learning algorithm utilized, and downstream tasks (i.e.,\n                    <jats:italic toggle=\"yes\">applications<\/jats:italic>\n                    ) of the learned representations. While examining the current landscape, we also identify limitations and challenges faced in the state-of-the-art, as well as promising future research directions in deep program representation learning.\n                  <\/jats:p>","DOI":"10.1145\/3769008","type":"journal-article","created":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T11:38:33Z","timestamp":1759923513000},"page":"1-37","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning Representations of Programs: A Systematic Literature Review"],"prefix":"10.1145","volume":"58","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-6164-3933","authenticated-orcid":false,"given":"Deepika","family":"Shanmugasundaram","sequence":"first","affiliation":[{"name":"University at Buffalo","place":["Buffalo, United States"]}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4716-4765","authenticated-orcid":false,"given":"Pallavi","family":"Arivukkarasu","sequence":"additional","affiliation":[{"name":"University at Buffalo","place":["Buffalo, United States"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5678-472X","authenticated-orcid":false,"given":"Huaming","family":"Chen","sequence":"additional","affiliation":[{"name":"The University of Sydney - Camperdown and Darlington Campus","place":["Sydney, Australia"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5224-9970","authenticated-orcid":false,"given":"Haipeng","family":"Cai","sequence":"additional","affiliation":[{"name":"University at Buffalo","place":["Buffalo, United States"]}]}],"member":"320","published-online":{"date-parts":[[2025,11,20]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"3","volume-title":"Proceedings of the International Conference on Artificial Intelligence in Education","author":"Abhinav Kumar","year":"2021","unstructured":"Kumar Abhinav, Vijaya Sharvani, Alpana Dubey, Meenakshi D\u2019Souza, Nitish Bhardwaj, Sakshi Jain, and Veenu Arora. 2021. 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