Natural Language Generation and Understanding of Big Code for AI-Assisted Programming: A Review

Man-Fai Wong; Shangxin Guo; Ching-Nam Hang; Siu-Wai Ho; Chee-Wei Tan

doi:10.3390/e25060888

Natural Language Generation and Understanding of Big Code for AI-Assisted Programming: A Review

Entropy (Basel). 2023 Jun 1;25(6):888. doi: 10.3390/e25060888.

Authors

Man-Fai Wong¹, Shangxin Guo², Ching-Nam Hang¹, Siu-Wai Ho³, Chee-Wei Tan⁴

Affiliations

¹ Department of Computer Science, City University of Hong Kong, Hong Kong, China.
² Shenzhen Research Institute, City University of Hong Kong, Shenzhen 518057, China.
³ Teletraffic Research Centre, University of Adelaide, Adelaide, SA 5005, Australia.
⁴ School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore.

Abstract

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.

Keywords: AI-assisted programming; large language models; software naturalness.

Publication types

Review

Grants and funding

This work is supported in part by the Ministry of Education, Singapore, under its Academic Research Fund (No. 022307 and AcRF RG91/22) and Google Faculty Award.