Natural Language Generation and Understanding of Big Code for AI-Assisted Programming: A Review
This paper provides a comprehensive review of the literature concerning the utilization of Natural Language Processing (NLP) techniques, with a particular focus on transformer-based large language models (LLMs) trained using Big Code, within the domain of AI-assisted programming tasks. LLMs, augment...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-06-01
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| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/25/6/888 |
| _version_ | 1797594976955138048 |
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| author | Man-Fai Wong Shangxin Guo Ching-Nam Hang Siu-Wai Ho Chee-Wei Tan |
| author_facet | Man-Fai Wong Shangxin Guo Ching-Nam Hang Siu-Wai Ho Chee-Wei Tan |
| author_sort | Man-Fai Wong |
| collection | DOAJ |
| description | This paper provides a comprehensive review of the literature concerning the utilization of Natural Language Processing (NLP) techniques, with a particular focus on transformer-based large language models (LLMs) trained using Big Code, within the domain of AI-assisted programming tasks. LLMs, augmented with software naturalness, have played a crucial role in facilitating AI-assisted programming applications, including code generation, code completion, code translation, code refinement, code summarization, defect detection, and clone detection. Notable examples of such applications include the GitHub Copilot powered by OpenAI’s Codex and DeepMind AlphaCode. This paper presents an overview of the major LLMs and their applications in downstream tasks related to AI-assisted programming. Furthermore, it explores the challenges and opportunities associated with incorporating NLP techniques with software naturalness in these applications, with a discussion on extending AI-assisted programming capabilities to Apple’s Xcode for mobile software development. This paper also presents the challenges of and opportunities for incorporating NLP techniques with software naturalness, empowering developers with advanced coding assistance and streamlining the software development process. |
| first_indexed | 2024-03-11T02:30:20Z |
| format | Article |
| id | doaj.art-fe8a5d66d6424a0ab58789ef20d2fa01 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-11T02:30:20Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-fe8a5d66d6424a0ab58789ef20d2fa012023-11-18T10:17:50ZengMDPI AGEntropy1099-43002023-06-0125688810.3390/e25060888Natural Language Generation and Understanding of Big Code for AI-Assisted Programming: A ReviewMan-Fai Wong0Shangxin Guo1Ching-Nam Hang2Siu-Wai Ho3Chee-Wei Tan4Department of Computer Science, City University of Hong Kong, Hong Kong, ChinaShenzhen Research Institute, City University of Hong Kong, Shenzhen 518057, ChinaDepartment of Computer Science, City University of Hong Kong, Hong Kong, ChinaTeletraffic Research Centre, University of Adelaide, Adelaide, SA 5005, AustraliaSchool of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, SingaporeThis paper provides a comprehensive review of the literature concerning the utilization of Natural Language Processing (NLP) techniques, with a particular focus on transformer-based large language models (LLMs) trained using Big Code, within the domain of AI-assisted programming tasks. LLMs, augmented with software naturalness, have played a crucial role in facilitating AI-assisted programming applications, including code generation, code completion, code translation, code refinement, code summarization, defect detection, and clone detection. Notable examples of such applications include the GitHub Copilot powered by OpenAI’s Codex and DeepMind AlphaCode. This paper presents an overview of the major LLMs and their applications in downstream tasks related to AI-assisted programming. Furthermore, it explores the challenges and opportunities associated with incorporating NLP techniques with software naturalness in these applications, with a discussion on extending AI-assisted programming capabilities to Apple’s Xcode for mobile software development. This paper also presents the challenges of and opportunities for incorporating NLP techniques with software naturalness, empowering developers with advanced coding assistance and streamlining the software development process.https://www.mdpi.com/1099-4300/25/6/888software naturalnesslarge language modelsAI-assisted programming |
| spellingShingle | Man-Fai Wong Shangxin Guo Ching-Nam Hang Siu-Wai Ho Chee-Wei Tan Natural Language Generation and Understanding of Big Code for AI-Assisted Programming: A Review Entropy software naturalness large language models AI-assisted programming |
| title | Natural Language Generation and Understanding of Big Code for AI-Assisted Programming: A Review |
| title_full | Natural Language Generation and Understanding of Big Code for AI-Assisted Programming: A Review |
| title_fullStr | Natural Language Generation and Understanding of Big Code for AI-Assisted Programming: A Review |
| title_full_unstemmed | Natural Language Generation and Understanding of Big Code for AI-Assisted Programming: A Review |
| title_short | Natural Language Generation and Understanding of Big Code for AI-Assisted Programming: A Review |
| title_sort | natural language generation and understanding of big code for ai assisted programming a review |
| topic | software naturalness large language models AI-assisted programming |
| url | https://www.mdpi.com/1099-4300/25/6/888 |
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